Synthesis of intelligent systems. Modern problems of science and education

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Sitnikov Mikhail Sergeevich. Analysis and synthesis of intelligent automatic control systems with fuzzy controllers: dissertation... Candidate of Technical Sciences: 05.13.01 / Sitnikov Mikhail Sergeevich; [Place of protection: Moscow. state Institute of Radio Engineering, Electronics and Automation]. - Moscow, 2008. - 227 p.: ill. RSL OD, 61 08-5/1454

Introduction

CHAPTER 1. Areas of application and research methods of intelligent automatic control systems with fuzzy controllers 14

1.1. Overview of application areas of ISAU with HP 14

1.2. Problems of researching ISAU with HP 24

1.3. Study of the influence of the main HP parameters on the nature of nonlinear transformations 28

1.3.1 The influence of the shape and relative placement of membership functions of individual terms on the nature of nonlinear transformations in the Mamdani fuzzy model 35

1.3.2 Influence of the order of relationships between input and output terms on the nature of nonlinear transformations in the Mamdani fuzzy model 41

1.4. Chapter 43 Conclusions

CHAPTER 2. Analysis and synthesis of intelligent automatic control systems based on the harmonic balance method 45

2.1. Study of ISAU using the harmonic balance method 46

2.2. Indirect quality assessment 73

2.3. The influence of fuzzy controller parameters on EKKU 81

2.4. Methods for research and synthesis of ISAU with HP based on the method

harmonic balance 90

2.5. Chapter 98 Conclusions

CHAPTER 3. Study of intelligent automatic control systems based on absolute stability criteria 99

3.1. Study of absolute stability of ISAU with HP 99

3.2. Study of the absolute stability of an automatic control system with several nonlinearities, 100

3.3. Study of the absolute stability of the equilibrium position of an automated control system with a fuzzy controller of the first type 105

3.4. Study of the absolute stability of processes in an automated control system with a fuzzy controller of the first type; 119

3.5. Study of the influence of fuzzy controller parameters on the absolute stability of the automated control system ". 124

3.6. Indirect assessments of the quality of ISAU regulation based on the criterion of absolute stability of processes 137

3.7. Chapter 139 Conclusions

CHAPTER 4. Automated synthesis of fuzzy controllers based on genetic algorithms 141

4.1. Review of automated synthesis methods 141

4.2. Using genetic algorithms to solve problems of automation of synthesis and tuning of fuzzy controllers 144

4.3. Algorithms for synthesis of automated control systems with HP 151

4.4. Methodology for automated synthesis and tuning HP 155

4.5. Chapter 167 Conclusions

CHAPTER 5. Software and hardware implementation of methods for analysis and synthesis of intelligent automatic control systems with fuzzy controllers 169

5.1. Software package for analysis and synthesis of ISAU with HP 170

5.2. Hardware implementation of an electric drive control system 177

5.3. Synthesis of HP ISAU for DC motor 180

5.4. Experimental studies 190

5.5. Chapter 199 Conclusions

References 203

Appendix 211

Introduction to the work

The use of intelligent technologies provides solutions to a wide range of adaptive control problems under conditions of uncertainty. At the same time, the software and hardware of such systems turn out to be simple and reliable, guaranteeing high quality control. The openness of such technologies allows for the integration of event forecasting mechanisms, generalization of accumulated experience, self-learning and self-diagnosis algorithms, thereby significantly expanding the range of functional capabilities of intelligent systems. The presence of a clear human-machine interface gives intelligent systems fundamentally new qualities that can significantly simplify the stages of learning and setting tasks.

One of the common intelligent technologies that has become widely used and has proven itself to be a convenient and powerful mathematical tool is the apparatus of fuzzy logic (FL). The theory of fuzzy sets and the logic based on it make it possible to describe imprecise categories, representations and knowledge, operate with them and draw appropriate conclusions and conclusions. The presence of such opportunities for forming models of various objects, processes and phenomena at a qualitative, conceptual level determined the interest in organizing intelligent control based on the use of this apparatus.

The results of theoretical and experimental studies show that the use of NL technology makes it possible to create highly efficient high-speed regulators for a wide class of technical systems used in industrial, military and household appliances, with a high degree of adaptability, reliability and quality of operation under conditions of random disturbances and uncertainty of external load.

Today, this apparatus is considered one of the most promising tools for describing special and non-standard cases that arise during the operation of the system. The peculiarity of the “fuzzy” representation of knowledge, as well as the unlimited number of input and output variables and the number of embedded rules for the behavior of the system, allow using this technology to form almost any control law, i.e. build a new type of nonlinear regulator, which distinguishes NL technology from others.

We will call the controller implemented using this technology fuzzy (HP). In the general case, HP is a frequency-dependent and nonlinear converter, which naturally raises a number of problems associated with studying the stability and quality of control of intelligent automatic control systems (AICS) with such controllers.

The most pressing problems that require solutions and ensure wider use of HP in engineering practice are:

Study of the features of nonlinear transformation in HP;

Development of engineering methods for studying the stability and quality of control of ISAU with HP;

Development of HP tuning and synthesis techniques;

Creation of tools to automate the HP setup procedure.

The subject of the research is nonlinear transformations implemented in HP, dynamic processes in automated control systems with HP, stability and quality of control of intelligent automatic control systems.

The object of study is intelligent automatic control systems with fuzzy controllers.

Goal of the work

Development of algorithmic, software and hardware tools for the research and synthesis of high-quality automated control systems with HP. To achieve this goal, the following tasks must be solved:

1. Investigate the features of the influence of HP parameters: number, type of membership functions (MF) and base of production rules (BP) on the nature of the nonlinear transformation carried out by it.

2. Based on methods known in TAU, develop mathematical models and corresponding engineering techniques for studying periodic processes, absolute stability and quality of automated control systems with HP.

3. Develop methods for synthesizing HP parameters based on given quality indicators of the automated control system.

4. Develop an algorithm for automated synthesis and adjustment of HP parameters to ensure stability and the required quality indicators of the automated control system.

5. Develop a software and hardware complex for designing an automated control system with HP.

The research methods in this work are based on the theory of automatic control, the theory of nonlinear systems, methods of mathematical and simulation modeling, graphic-analytical methods for solving problems, the theory of fuzzy logic, optimization theory and the theory of genetic algorithms.

The validity and reliability of scientific statements, conclusions and recommendations is confirmed by theoretical calculations, as well as the results of numerical modeling and the results of experimental studies. The results of modeling in the Matlab environment, experimental studies of the control system in the Simulink environment and on the hardware-software complex for designing ISAU fully confirm the theoretical provisions and recommendations of the dissertation work and allow them to be used in the design of real ISAU. Main provisions submitted for defense

1. Results of a study of the features of the influence of HP parameters (number, type of FP and BP) on the nature of its nonlinear transformations.

2. Mathematical model for studying periodic oscillations and control quality in automated control systems with HP based on the harmonic balance method.

3. Criteria for the absolute stability of processes and the equilibrium position of the automated control system with HP.

4. Engineering methods for studying periodic oscillations, indirect assessment of control quality and absolute stability of automated control systems with HP.

5. Method of synthesis of HP automated control systems with a given quality of control.

6. Algorithm for automated synthesis and adjustment of HP parameters using genetic algorithms.

7. Hardware and software complex for designing ISAU with HP. Scientific novelty

1. The dependence of the characteristics of the nonlinear HP transformation on the parameters of fuzzy calculations (type and location of membership functions, base of production rules) is substantiated.

2. Mathematical models have been developed that allow using the harmonic balance method to study periodic oscillations and the quality of control of the automatic control system.

3. Criteria for the absolute stability of processes and the equilibrium position in an automated control system with HP have been developed.

4. On the basis of genetic algorithms, the problem of automated synthesis and adjustment of HP parameters has been solved, taking into account the required quality of ISAU control.

Practical value

1. Convenient engineering methods have been developed for studying periodic oscillations and indirectly assessing the quality of control of automated control systems with HP based on the harmonic balance method.

2. Convenient engineering methods have been developed for studying the absolute stability of processes and the equilibrium position in automated control systems with HP.

3. A methodology for automated synthesis and adjustment of HP parameters has been developed, taking into account the areas of stability and quality of the automated control system.

4. A hardware and software complex has been created for the research and design of ISAU with HP.

5. The results of the dissertation work were used in the research project “Latilus-2”, carried out on the instructions of the SPP at the Presidium of the Russian Academy of Sciences, “Exploratory research and development of intelligent methods for precision control of actuators of promising weapons and military equipment.” In particular, it has been shown that the use of HP, which implements a nonlinear control law, can significantly improve the quality of control of actuators of new models of military equipment (performance increases by 2-3 times, overshoot is reduced by 20%). The control error caused by the load can be reduced several times.

Convenient graphic-analytical methods for the analysis and synthesis of automated control systems with HP for actuators and promising models of military equipment are proposed.

6. The results of the dissertation work were used to carry out work under grants from the Russian Foundation for Basic Research:

2005-2006, project number 05-08-33554-a “Development of mathematical models and methods of harmonic balance for the study of periodic processes and quality of control in fuzzy systems.”

2008-2010, project number 08-08-00343-a “Automated synthesis of fuzzy controllers based on genetic algorithms.”

Approbation of work. The main provisions of the work were discussed and presented at a conference on robotics in memory of academician E.P. Popov (MSTU named after N.E. Bauman 2008), at the XIV and XV international scientific and technical seminars “Modern technologies in problems of control, automation and information processing” (Alushta 2006-2007), at the XV International Student School -seminar "New Information Technologies" (Sudak 2006), at the I All-Russian Scientific Conference of Students and Postgraduate Students "Robotics, Mechatronics and Intelligent Systems" (Taganrog 2005), at the All-Russian Review-Competition of Scientific and Technical Creativity of Students of Higher Educational Institutions " EUREKA-2005" (Novocherkassk 2005), at the scientific and practical conference "Modern information technologies" in management and education. (Voskhod) Moscow 2006

Publications

The main results of the dissertation work were published in 8 printed works, including one article in a journal from the list of the Higher Attestation Commission and one monograph.

In the first chapter, based on a review of the areas of application of HP systems, their widespread use in various fields of science and technology is shown. A number of advantages are shown, including high quality management, efficiency and functionality.

At the same time, it is shown that today there are no methods and techniques convenient for engineering practice that allow for a full cycle of analysis and synthesis of automated control systems with HP.

The chapter examines the features of the influence of HP parameters (number, type of FP and BP) on the nature of its nonlinear transformation between the signals at the input and output. The conducted research, on the one hand, is a necessary basis for the adequate application of methods for studying nonlinear systems to the study of automated automated control systems with HP and, in particular, the harmonic balance method and criteria for absolute stability, and on the other hand, solving the problem of synthesizing automated control systems with given properties is possible only with Understanding the dependence of nonlinear transformation on HP settings.

Based on the research carried out, the objectives of the dissertation work are justified.

In the second chapter, mathematical models are developed that make it possible to study periodic oscillations in an automated control system with HP using the harmonic balance method. The possibility of indirectly assessing the quality of automated control systems with HP based on the harmonic balance method based on the oscillation index is also substantiated, and an appropriate methodology is developed.

The problem of synthesizing an automated control system with HP with specified quality indicators based on the harmonic balance method has been solved.

The chapter explores and shows the influence of the form of membership functions and the relative placement of terms, as well as the influence of production rules on the nature of the HP ECC.

The results of experimental studies on computer models confirmed the adequacy of the developed methods for analysis and synthesis of automated control systems with HP based on the harmonic balance method.

In the third chapter, mathematical models are developed that make it possible to transform the structure of an automated control system with HP of the first type to the structure of a nonlinear multi-circuit automatic control system. Taking into account the nature of nonlinear HP transformations, based on the criteria for the absolute stability of processes and the equilibrium position for systems with several nonlinearities, corresponding criteria for automated control systems with HP of the first type have been developed.

Based on the proposed criteria, a graphic-analytical technique has been developed for studying the stability of the equilibrium position and processes in an automated control system with HP.

To solve the problems of ISAU synthesis, a study was carried out to study the dependence of the absolute stability areas of the ISAU on HP parameters (the type and number of PTs and PSUs).

Based on the criterion of absolute process stability, a method for indirectly assessing the quality of automated control systems with HP has been developed.

Studies were carried out on computer models, the results of which confirmed the adequacy of the developed methods for studying the absolute stability of the equilibrium position and processes in an automated control system with HP.

The fourth chapter is devoted to the development of algorithms and methods for automated synthesis of HP parameters in ISAU. The analysis carried out in the dissertation showed that genetic algorithms (GA) are by far the most promising technology for solving this problem. When developing an automated synthesis algorithm, the following problems were solved: synthesis of an ISAU simulation model; selecting initial HP parameters and GA search parameters; assessing the quality of ISAU management; chromosome coding. The example shows the performance of the automated synthesis algorithm.

The fifth chapter tests the theoretical results obtained in chapters 2-4. A hardware and software complex is being developed that allows for a full cycle of designing fuzzy controllers, starting with the development of mathematical models and ending with direct testing on real equipment. The chapter develops and presents a software package for the analysis and synthesis of ISAU models with HP. The structure of interaction between the software and hardware (stand) parts of the complex has been implemented, allowing for full-scale experiments on controlling a DC motor under various types of loads and disturbances

The chapter presents the results of experimental studies, including automated synthesis of HP parameters, with testing on a real bench, as well as a comparative assessment of the tuning results for the quality of control of an automatically tuned automated control system with HP and an automatic control system with a PID controller tuned using the method of inverse dynamics problems (IDP).

In conclusion, the main scientific and practical results of the dissertation work are presented.

Study of the influence of the main HP parameters on the nature of nonlinear transformations

Despite its widespread use and popularity, the use of the NL apparatus is associated with significant difficulties. First of all, this is due to the lack of complete engineering tools for analyzing the quality of functioning of fuzzy systems, as well as studying their stability.

Against the background of the lack of effective methods for analyzing fuzzy systems, the problem of HP synthesis arises even more acutely, since the dependence of the influence of its parameters on the quality of operation of the automated control system has been studied rather poorly. These factors significantly hinder the wider introduction of HP into the practice of creating new self-propelled guns.

The first Lyapunov method makes it possible to analyze the quality of control using linearized ACS equations and can be applied to systems of any structure. This method allows us to obtain the necessary conditions for the stability of the system in small quantities, but for large deviations of the system it does not guarantee stability. It requires linearization of nonlinear elements included in the ACS, therefore it is suitable only for analyzing ACS with primitive fuzzy calculations.

The second Lyapunov method allows one to obtain sufficient stability conditions. It is assumed that an automated control system with a fuzzy controller is described by a system of nonlinear differential equations of the first order and on this basis, taking into account the specifics of the nonlinear transformation, a special Lyapunov function is constructed, the properties of which allow one to analyze the stability of the system under study and determine some quality indicators. The problems of using this method include the difficulty of choosing a function appropriate to the system, which also includes the representation of fuzzy calculations. Some of the first works in relation to specific systems with HP are.

As a note, it should be noted that the most widely used among the NV algorithms (Mamdani, Tsukamoto, Takagi-Sugeno (T-S), Larsen) are Mamdani and Takagi-Sygeno. To study ISAU with HP built using the T-S algorithm, an analytical method of the same name for studying the stability of Takagi-Sygeno, based on the second Lyapunov method, was developed. This method does not apply to systems with NV built using the Mamdani algorithm.

The approximate harmonic balance method, based on the filter hypothesis, allows one to study self-oscillations in a fuzzy system. This method is graphic-analytical and allows you to study the automated control system without representing HP in analytical form, using only the characteristic of its nonlinear transformation. It was first used to analyze ISAU with HP and expanded by the authors. As a rule, it was used to analyze certain automated control systems that included a fuzzy P-controller, and in relation to the automated control system with a frequency-dependent fuzzy controller (PI-FID), the studies had a very rough assessment of the dynamic properties of the system. It should also be noted that the approach proposed in the works lacks a methodological character that would allow, on its basis, to develop engineering tools for the analysis of such automated control systems.

When studying the stability of fuzzy systems, a method based on absolute stability criteria (circular criterion and V.M. Popov’s criterion) was also used. To use this method, it is necessary to conduct additional studies of the dependence of the nonlinear characteristic to satisfy a number of requirements. As a rule, it was used to analyze a specific automated control system with fuzzy P-controllers.

Work has also been carried out on the study of fuzzy systems using various approximate methods.

Apparently, a relatively small number of works have been devoted to the study of the stability of automated automated control systems with HP and, as a rule, all of them are of a private, non-systemic nature. This essentially speaks of the initial stage of development in this direction and involves more in-depth research into the capabilities of each of the listed methods. One of the first attempts at a systematic approach to the study of fuzzy systems belongs to the authors of a work published in 1999. In this work, fuzzy systems are reduced to nonlinear ones, and on this basis, methods designed to study the stability of nonlinear systems are applied to them. As the authors themselves note, the work has several significant shortcomings, the first of which is a rather superficial approach to the analysis of fuzzy systems, because no clear, systematic methods of analysis using the presented methods are shown. Also, due attention has not been paid to the analysis of the influence of NV parameters on nonlinear HP transformations. The work does not present any tools for the synthesis and configuration of fuzzy automated control systems, which is very important for their practical application. Recent published works devoted to the analysis of automated control systems with HP are mainly based on the above methods.

Study of ISAU using the harmonic balance method

As was shown in the previous chapter, an intelligent controller carries out some nonlinear transformation, as a result of which it becomes possible to improve the quality of control in such systems. But at the same time, the presence of nonlinear elements in the ACS circuit, as is known, can lead to various problems associated with the dynamics of the system. In particular, the stability regions on the plane of system parameters change (compared to linear systems), and it is necessary to study both equilibrium positions and processes. The study of periodic regimes characteristic of nonlinear systems becomes important.

For the study of periodic oscillations in automated control systems, the harmonic balance method seems promising, which has found wide application in the engineering practice of analysis and synthesis of nonlinear automatic control systems.

This method allows not only to study periodic oscillations in automatic control systems, but also to indirectly assess the quality of control of nonlinear systems. The last aspect is extremely important from the point of view of the prospects for solving the ambiguous problem of tuning a fuzzy controller to the required quality of control.

Since intelligent automatic control systems, as has been repeatedly noted, are designed to provide alternative control algorithms for complex dynamic objects operating under the influence of internal and external factors of uncertainty, it should be emphasized that these objects, as a rule, have a fairly high dimension and, therefore, to a large extent satisfy the requirements of the filter hypothesis. And hence the accuracy of the results, which the harmonic balance method will provide, may turn out to be quite acceptable for practical use.

When studying intelligent systems using the harmonic balance method, a methodological problem arises due to the fact that it was developed for an automatic control system with one nonlinear element having one input and one output, and in an automatic control system with HP there are several such nonlinear elements, so it is necessary to build an HP model, allowing you to apply the harmonic balance method.

In the general case, we present the block diagram of an intelligent automatic control system with a fuzzy controller (HP) in the form of a serial connection of a fuzzy computer (FC) having h - inputs with linear dynamic links connected to them, and one output, and a control object (OU) with a transfer function Woy(s) (Fig. 2.1), where g(t) is the command signal (for mechanical systems this is position, speed, acceleration, etc.), u(t) is the control signal, y(t) is the output signal of the actuator, e(t) is the control error signal, s is the Laplace operator.

A fuzzy controller can be built on the basis of two types of structures: the first type - a fuzzy controller with parallel one-dimensional fuzzy computers НВІ (in Fig. 2.2, for example, the block diagram of a fuzzy PID controller of the first type is shown) and the second type - with a fuzzy computer with a multidimensional input (Fig. 2.3 shows a block diagram of a fuzzy PID controller of the second type).

Taking into account the nonlinear nature of the transformations in HP, shown in the first chapter, to study periodic oscillations in the automated control system we will use the harmonic balance method.

To apply the harmonic balance method, we will consider the fuzzy controller as a nonlinear frequency-dependent element with one input and one output. The study of self-oscillations in the ISAU presented in Fig. 2.1 will be carried out at g(t) = 0. Let us assume that a sinusoidal signal e(t) = A sin a t operates at the HP input. The spectral representation of the output signal HP is characterized by terms of the Fourier series with amplitudes U1, U1, U3... and frequencies CO, 2b), bco, etc. Taking into account the fulfillment of the filter hypothesis for the ISAU control object, we will assume that in the spectral decomposition of the signal y(f), at the output of the control object, the amplitudes of the higher harmonics are significantly less than the amplitude of the first harmonic. This allows us, when describing the signal y(t), to neglect all higher harmonics (due to their smallness) and assume that y(t) s Ysm(cot + φ).

Study of absolute stability of ISAU with HP

In the previous chapter, the harmonic balance method was considered for solving problems of analysis and synthesis of small-scale intelligent automatic control systems with sequential controllers. Despite the known limitations of this method, the results of studying self-oscillations on the plane of control system parameters in many cases provide a comprehensive result at the analysis stage and quite constructive approaches to the synthesis of controller parameters for a given oscillation indicator.

At the same time, it is known that for many nonlinear control systems, the study of only periodic movements is incomplete and does not adequately reflect the dynamic processes in the system. Therefore, it is undoubtedly of interest to develop methods that make it possible to study the absolute stability of both the equilibrium position and processes in intelligent control systems.

Taking into account the features of nonlinear transformations carried out in intelligent controllers discussed in Chapter I, it can be assumed that today the development of methods for studying absolute stability seems most realistic for automated control systems with fuzzy controllers of the first type, since such systems can be reduced to multi-loop nonlinear systems, methods studies of which are described in the literature.

Since an automated control system with HP of the first type is, in the general case, a nonlinear multi-loop system, it is advisable to first consider the known criteria for the absolute stability of the equilibrium position and processes for this kind of nonlinear systems.

A generalized block diagram of a multi-circuit nonlinear automatic control system is shown in Fig. 3.1, in which % and a are scalar vectors.

Let us denote by u(V the class of nonlinear blocks (3.3), which have the following properties: for h \ inputs o-jit) and outputs %.(t) of nonlinear blocks are connected (for ov (/) 0) by the relations: %) "" and = 1 m (3-9) where cCj,fij are some numbers. In addition, the matrix inequality \j3 (t)(t)) 0 must be satisfied. (3.10) The circular criterion for the absolute stability of processes for systems with several nonlinearities (Fig. 3.1.) has the following formulation:

Let the equations of the linear part of the system have the form (3.1) and the equations of the nonlinear blocks (3.3). Let all the poles of the elements of the matrix Wm(s) be located in the left half-plane (stable linear parts in all contours), a = diag(al,...,ah), f$ = diag(pl,...,J3h) - diagonal matrices with specified diagonal elements. Let us assume that for some hxh diagonal matrix d with positive diagonal elements the frequency condition te B(N »_N Fig. 3.2.b.

It should be taken into account that the linear part of the system will also change. Thus, taking into account the above features of the criterion for the absolute stability of an equilibrium position for multidimensional nonlinear systems, let us formulate it for an automated control system with HP.

As already noted in the first chapter, NV carries out a nonlinear transformation. It should be noted that the nonlinear characteristics %(&), implemented by fuzzy calculators, have limitations in amplitude, therefore, when Уj - the lower boundary of the sector can be equated to zero a = O, which follows (p (a) o ? -±L = juJ pj, j = \,...,h (3.14) if U F O I 3(0) = 0, or (j3a(t)-cp(o;t))(p(cr, t) 0. (3.15)

If, in the process of setting up a fuzzy controller of the first type, it turns out that one of the fuzzy computers implements nonlinear transformations (Pji j) (Fig. 3.3a) that do not satisfy the conditions of class G \ then it is necessary to carry out structural transformations in accordance with Remark 3.4. Naturally, in order to preserve the condition of equivalence of the original and transformed structures, it is necessary to make appropriate changes to the linear part.

In the case of the presence of a neutral linear part in one of the ISAU circuits (Fig. 3.4), in order to apply the criterion of absolute stability of the equilibrium position (3.7), it is necessary to cover with negative feedback є 0 both the corresponding linear part and HBj with the nonlinear characteristic Pj(crj ). At -»0, criterion (3.7) will be applicable for all frequencies except co = 0. Taking into account the above, the criterion for the absolute stability of the equilibrium position for an automated control system with HP of the first type will be written in the following form.

Let the equations of the linear part of the ISAU have the form (3.1), the nonlinear characteristics of the fuzzy controller correspond to (3.3), where the functions (PjiGj) satisfy the conditions of class G. Let all the poles of the elements of the matrix Wm (s) be located in the left half-plane or have one pole on the imaginary axis (stable or neutral linear parts in all contours). Let us introduce a diagonal matrix /Jj = diag(jti[ ,..., juh) with diagonal elements ju ,...,juh , and Mj = if Mj =, as well as diagonal matrices rd = diag(Tx,..., rh), 3d =diag(3l,...,3h), where all Td 0. Suppose that for some m 0, 3= and all - oo with +oo, except oo = 0, the following relations hold:

Using genetic algorithms to solve problems of automating the synthesis and tuning of fuzzy controllers

The implementation of the procedure for automated synthesis of HP parameters based on GA necessitates the solution of three main tasks: 1) determination of the functional features of the GA operation; 2) determination of the method for encoding HP parameters into the chromosome; 3) implementation of the target function.

Standard genetic algorithms, by definition, operate with a set of elements called chromosomes; in this work, they are bit strings with an encoded description of potential solutions to a given applied problem. In accordance with the generalized block diagram for constructing a genetic algorithm (Fig. 4.1), within its next cycle, each of the chromosomes of the existing set is subjected to some assessment based on an a priori specified “utility” criterion. The results obtained allow us to select the “best” specimens to generate a new population of chromosomes. In this case, the reproduction of descendants is carried out due to random changes and cross-breeding of the corresponding bit strings of the parent individuals. The evolution process is stopped when a satisfactory solution is found (at the stage of assessing the utility of chromosomes), or after the allotted time has elapsed.

It should be noted that the inheritance of the characteristics of elite representatives of the previous population in the next generation of individuals provides an in-depth study of the most promising areas of the solution search space. At the same time, the presence of mechanisms for random mutation of bit strings of selected elements guarantees a change in search directions, preventing hitting a local extremum. Such an imitation of evolutionary processes makes it possible to ensure the convergence of the search procedure to the optimal solution, but its effectiveness is largely determined by the parameters of the genetic algorithm and the set of initial data specified taking into account the specifics of the applied problem. These include the type and dimension of the chromosome, population size, the function for assessing the utility of chromosomes and the type of selection operator, the criterion for stopping the search procedure, the probability of performing a mutation, the type of crossing operation, etc. HP Parameter Coding

Despite the apparent simplicity of constructing and implementing genetic algorithms, their practical application is also associated with the complexity of choosing a method for encoding the search space for solutions to a specific applied problem in the form of a chromosome with the further formation of an objective function, the calculation of the value of which will be used to evaluate and subsequently select individual individuals in the current generation for automatic generation of the next one.

Thus, when synthesizing fuzzy controllers in accordance with the Mamdani scheme, the set of tuning parameters that allow obtaining the required quality of control includes the number and relationships of terms of input and output linguistic variables (LP), as well as the form of membership functions (MF) and their placement within the working range.

In any case, the structure and dimension of the chromosome encoding HP parameters must be determined taking into account a number of specific factors, including those characterizing the chosen method of representing membership functions.

Stepanov, Andrey Mikhailovich

1

The paper considers the problem of synthesizing an intelligent multi-purpose control system. Given a mathematical model of a control object, a control goal, a quality criterion, and limitations, it is necessary to find a control that ensures the achievement of several goals and minimizes the value of the quality criterion. Control goals are specified in the form of state space points that must be achieved during the control process. A special feature of the problem is that we are looking for control in the form of two multidimensional functions of different types of state space coordinates. One function ensures that the object achieves a private goal, and the other function, logical, ensures that private goals are switched. To solve the problem of multi-objective control synthesis, the network operator method is used. When solving the main synthesis problem, together with the synthesizing functions for each subtask, we define a selection function that ensures control switching from solving one subtask to solving the next subtask.

network operator.

intelligent control

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2. Diveev A.I. Synthesis of an adaptive control system using the network operator method // Questions of the theory of security and stability of systems: Coll. articles. M.: Computer Center RAS, 2010. Issue. 12. pp. 41-55.

3. Diveev A.I., Sofronova E.A. Identification of a logical inference system by the network operator method // Vestnik RUDN. Series Engineering Research. 2010. No. 4. P. 51-58.

4. Diveev A.I., Severtsev N.A. Network operator method for synthesizing a spacecraft descent control system under uncertain initial conditions // Problems of mechanical engineering and machine reliability. 2009. No. 3. P. 85-91.

5. Diveev A.I., Severtsev N.A., Sofronova E.A. Synthesis of a meteorological rocket control system using the genetic programming method // Problems of mechanical engineering and machine reliability. 2008. No. 5. P. 104 - 108.

6. Diveev A.I., Shmalko E.Yu. Multicriteria structural-parametric synthesis of a spacecraft descent control system based on the network operator method // Vestnik RUDN. Engineering Research Series (Information Technology and Management). 2008. No. 4. P. 86 – 93.

7. Diveyev A. I., Sofronova E. A. Application of network operator method for synthesis of optimal structure and parameters of automatic control system // Proceedings of the 17th IFAC World Congress, Seoul, 2008, 07/05/2008 – 07/12/2008. P. 6106 – 6113.

Let us consider the problem of synthesizing a control system with several control objectives.

A system of ordinary differential equations is specified that describes the model of the control object

where , , is a bounded closed set, .

We estimate the state of the control object based on the observed coordinates

For system (1) the initial conditions are given

Set of target states

, (4)

Management quality criterion has been set

, (5)

where is the control time, which can be limited, but not specified.

It is necessary to find control in the form

which ensures successive achievement of all target points (4) and minimizes the functionality (5).

The purpose of management (4) is multi-valued. To move on to the task of synthesizing an intelligent control system, it is necessary to provide the system with the ability to choose. For this purpose, we weaken the requirements for the object to hit each target point and replace it with the requirement for it to hit the vicinity of the target point.

Then we have a trade-off between accuracy and speed of reaching target points. To implement control in this problem, we need to solve each time the problem of choosing between accurately achieving the current goal and moving to another goal. Obviously, under this condition, in the control system, in addition to the feedback regulator that ensures the achievement of the goal, it is necessary to have a logical block that switches goals.

Let us clarify this statement of the problem.

Let us represent control (6) as a function depending on the distance to the target

(8)

where is the number of the current target point.

At any time, the number of the current target point is determined using a logical function

, , (9)

Where , , - predicate function,

: . (10)

Function (10) also needs to be found together with the synthesizing function (6). Function (10) should ensure switching of target points. Both functions (6) and (10) must provide a minimum of the quality functional (5) and the accuracy functional

, (11)

The control time is determined by reaching the last target point

If , (12)

where is a small positive value.

We replace the partial criterion (5) with the overall quality criterion

(13)

To construct a predicate function, we use the discretization function and the logical function.

, (14)

where is a logical function,

: , (15)

Where , , - sampling function.

The task is to find controls in the form

where is an integer vector defining controls for solving a particular problem. Control (16) must ensure that the minimums of functionals (11) and (13) are achieved.

In the general case, since the problem contains two criteria (11) and (13), its solution will be the Pareto set in the space of functionals. The developer selects a specific solution for the Pareto set based on the results of modeling and research of the synthesized control system.

We call task (1) - (3), (7) - (16) the task of synthesizing an intelligent control system. To solve it, it is necessary to find two multidimensional synthesizing functions and .

To solve the problem of synthesizing an intelligent control system, we use the network operator method. To find a function, we use the usual arithmetic network operator, in which we use a set of arithmetic functions with one or two arguments as constructive functions. In the network operator method, these functions are called unary or binary operations. To find a logical function, we use a logical network operator, respectively, with unary and binary logical operations.

As an example, consider the following mathematical model

where , are coordinates on the plane.

There are restrictions on management

The trajectory of movement is specified by a set of points.

It is necessary to find a control to minimize two objective functions of the object. The first functional determines the accuracy of movement along the trajectory, and the second determines the time it takes to complete the trajectory.

S. Oreshkin, A. Spesivtsev, I. Daymand, V. Kozlovsky, V. Lazarev, Automation in industry. 2013. No. 7

A new solution to the problem of constructing an intelligent automated process control system (IASTP) is considered, combining the use of unique methodologies: the construction of a semantic network on a basic ontology and the polynomial transformation of NON-factors, the essence of which is to transform the qualitative knowledge of an expert into a mathematical model in the form of a nonlinear polynomial function.

The Summa Technologies company proposes a new solution to the problem of building an intelligent automated process control system (IASTP), combining the use of unique methodologies: the construction of a semantic network on a basic ontology, which allows you to describe a complex multifactor model in the form of a semantic network on a specific limited dictionary, and a polynomial transformation of NON-factors, the essence of which is to transform the expert’s qualitative knowledge into a mathematical model in the form of a nonlinear polynomial function. The first of the methodologies has the property of universality regardless of the subject area, and the second conveys the specifics of this area through the experience and knowledge of experts. The results of industrial tests of the developed IAS are presented in relation to the smelting process of sulfide copper-nickel raw materials at the Copper Plant of the Polar Division of OJSC MMC Norilsk Nickel (Norilsk), which has the properties of a “complex system” and operates under conditions of “significant uncertainty.”

Introduction

Analyzing the tasks of automated control of most technological processes in various industries (chemical, ferrous and non-ferrous metallurgy, mining, oil and gas production, thermal power engineering, agriculture, etc.), we can highlight the problem that unites them, which is the need to build a mathematical model of technological processes that will allow take into account all the required input information, taking into account its possible inaccuracy, uncertainty, incompleteness, and at the same time obtain output data (control action, forecast) that is adequate to the current situation in the technological process.

It is known that the traditional approach to modeling (that is, modeling based on traditional methods under the assumption of completeness and accuracy of knowledge about the process) is practically inapplicable when considering complex multifactor processes that are generally difficult to formalize. The complexity of real processes determines the search for unconventional methods for constructing their mathematical models and optimizing their control. In this case, not only the aspect of optimal control is very important, but also the aspect of analyzing the current state of the process, since it is the conclusion about the current state of the process that allows you to choose the optimal control in a given situation. Such an analysis can be performed on the basis of a system of structural-flow-multilevel recognition of the technical state of a process in real time.

The main factor that devalues ​​attempts to build formal models and describe the technical state of such complex processes using traditional methods is the “significant uncertainty” of the input information. This is manifested in the objective impossibility of stabilizing and/or measuring the values ​​of a number of key parameters of the technical state of such processes. The consequence of this is a violation of the main criteria for the technological consistency of the process, which affects both the quality of the final products and the stability of the process as a whole. In the language of mathematics, such processes are classified as “complex technical systems” or “weakly structured systems,” for which there is currently no general modeling theory.

A traditional process control system aims to automate the maintenance of a unit or processing unit, and its functions, by definition, do not include issues of optimal process control and analysis of its condition. For example, an automated process control system allows you to change the position of the control mechanisms that serve the unit, monitors the connected operation of the units of the unit, and allows you to change the performance of the unit and its operating mode. But the state of the process, the quality of the final products, the ratio of incoming products by elemental composition - these issues are often outside the basic automation of the unit. Thus, if there is only a basic process control system, the operator is forced to perform maintenance functions not only of the unit, but also of the process occurring in it. This is precisely what leads to the problem of the “human factor”, since the operator does not always manage to fully achieve all, most often multidirectional, control goals. In addition, the design features of the unit do not always allow all issues to be fully resolved at the process control system level. An example of this is the problem of ensuring in the current version of the process control system the necessary reliability of input information when assessing the quality and quantity of materials supplied to the reaction zone in real time.

An intelligent automated control system (IACS) is a system that uses the basic automation of a unit as a source of input information and allows, based on artificial intelligence technologies, to build a model of the process occurring in the unit, analyze the current state of the process using the model and, based on the analysis, solve the problem of optimal control of a given unit.

Existing so-called turnkey “off-the-shelf solutions” presuppose the need for complete automation of a unit or processing unit “from scratch.” In this case, the customer is supplied with both the automation hardware component and the software. The functionality of such a solution can be quite broad, including containing an intellectual component, but at the same time completely incompatible with the customer’s currently existing process control systems. This often leads to a sharp increase in complexity and cost of the technical solution. The proposed option for building an intelligent automated control system based on expert knowledge, using basic automation, aims to monitor and control the process occurring in the unit. Such a system, under conditions of “significant uncertainty,” is capable of assessing unmeasured or poorly measured parameters, interpreting them quantitatively quite accurately, identifying the current technical state of the process and recommending the optimal control action to eliminate the conflict that has arisen (if there are conflicts in the technological consistency of the process).

IASU in this version, using intelligent technologies, allows you to:

• carry out integration with any basic automated control system that already exists on the customer’s unit or processing unit;
• implement the creation of a common information space for all processing units in order to implement general management and monitoring;
• perform a quantitative assessment of unmeasured and/or qualitative parameters on each unit within the framework of the basic automated control system of the unit;
• monitor the criteria for technological consistency of the process both for each individual unit and (if necessary) for the processing unit as a whole;
• assess the current state of technological processes both for each individual unit and for the processing unit as a whole in real time;
• develop control decisions - advice to the operator regarding the restoration of the technological balance both for the unit and for the processing unit as a whole.

The basis of the intellectual core of the IASU is the method of representing knowledge “Semantic network on a basic ontology”, which allows you to describe a complex multifactor model in the form of a semantic network on a specific limited dictionary, and the method “Polynomial transformation of NON-factors”, the essence of which is to transform the qualitative knowledge of an expert into mathematical model in the form of a nonlinear polynomial function.

The purpose of this article is to familiarize readers with a new approach to solving the problem of constructing an automated control system, based on the use of unique methodologies, and the results of the industrial operation of the automated control system PV-3 of the Copper Plant of the Polar Division of OJSC MMC Norilsk Nickel. IASTP was developed by the Summa Technologies company in 2011–2012. based on the G2 platform from Gensym (USA) to control the Vanyukov process for processing sulfide copper-nickel raw materials.

Technological process as an object of modeling

Most technological processes, including the Vanyukov process, have all the signs of “complex technical systems” - multiparameters and “significant uncertainty” of input information. In such conditions, to solve the problem of maintaining the technological consistency of the technological process, it is advisable to use methods of expert assessment of the situation and the formation of a conclusion based on the knowledge and experience of the expert.

The Summa Technologies company developed the IASU Vanyukov Furnace (IASU PV-3) of the Copper Plant of the Polar Division of OJSC MMC Norilsk Nickel based on the G2 platform from Gensym (USA) to solve the following problems of controlling the Vanyukov process:

• stabilization of the quality of smelting products;
• quantitative assessment of unmeasured or poorly measured (due to a number of both objective and subjective reasons) parameters of the technological process and states of units using indirect methods;
• reducing the energy intensity of the process of processing various charge materials;
• stabilization of the temperature regime of the process while maintaining planned assignments and goals.

In Fig. Figure 1 shows the layout of the main structural elements of the PV. The unit is a rectangular caissoned water-cooled shaft 2 located on the bottom 1, in the roof of which there are two chutes 3 for supplying charge materials to the melt, and to which matte 4 and slag 5 siphons with drain holes 9 and 10 are adjacent, respectively, from the end walls. To evacuate gases, an uptake 6 is provided. The charge materials through chutes 3 enter the melt, which is blown with an oxygen-air mixture (OAC) through tuyeres 7, intensively bubbling the matte-slag emulsion in the above-tuyere zone. Oxygen from the mixture oxidizes iron sulfide, thereby enriching the matte “kinglets” (drops), which segregate to the bottom due to the difference in the densities of the immiscible liquids of matte and slag. In this case, the movement of the melt mass flows is directed downward due to the continuous release of matte 4 and slag 5 from the siphons through outlets 9 and 10, respectively. Thanks to the design features shown in Fig. 1, the Vanyukov process itself is implemented, the main idea of ​​which is clear from the above description.

It is worth noting the features of the Vanyukov process that distinguish it from other, including foreign, pyrometallurgy technologies: high specific productivity - up to 120 tons per 1 m2 of bath surface area per day (melting up to 160 t/h); small dust removal -< 1%; переработку шихты крупностью до 100 мм и влажностью > 16%.

The software and hardware complex, on the basis of which the automated process control system PV-3 is implemented, has a three-level architecture. The lower level includes sensors, electric drives, control valves, actuators, the middle level - PLC, the upper level - personal electronic computers (PCs). Based on the workstation, a graphical interface for interaction between the operator and the control system, an audio alarm system, and storage of process history are implemented (Fig. 2).

The smelting process is controlled from the operator's workstation (“remote panel”). In this case, not only information from sensors and actuators is used, but also organoleptic information, when the melter, observing the characteristic features of the behavior of the melt bath (the size and “heaviness” of the splashes, the general condition of the bath, etc.), transmits the resulting assessments to the operator’s console. All these sources of information, heterogeneous in their physical nature, together allow the operator to assess the current situation based on many variables, for example, “Loading”, “Bath height”, “Melt temperature”, etc., which determine more general concepts: “State of the melt bath”, "State of the process as a whole".

Objectively emerging production conditions often lead to stricter requirements for the Vanyukov process; for example, the need to melt a large amount of man-made raw materials, which significantly complicates the task of maintaining technological consistency of the process, since man-made components are poorly predictable in composition and humidity. As a result, the operator, not having sufficient information about the properties of such raw materials, is not always able to make the right decisions and “loses” either the temperature or the quality of the final products.

The basis of the developed IASU PV-3 is the principle of conducting the process in a fairly narrow “corridor” according to the main criteria of technological consistency of the process to improve the quality of the final product and maintain the operational properties of the unit. IASU PV-3 is designed for early prediction and informing the operator about violations of technological consistency at the initial stages of their occurrence by analyzing special criteria developed on the basis of expert knowledge. Criteria set the goals for process control and inform the operator about the current state of the process. In this case, the departure of the criteria values ​​beyond the permissible limits is interpreted by the system as the beginning of a “conflict”, and for the operator it is a signal of the need to take recommended control actions to return the process to a state of technological consistency.

Brief description of system capabilities

IASU PV-3, based on initial information received from ACS PV-3 and other information systems, implements the Vanyukov process model in real time, analyzes the current state of the process for the presence of technological imbalances and, in case of conflicts, identifies them, offering conflict resolution scenarios to the operator. The system thus acts as an “adviser to the operator.” The automated control system visualizes information channels that display to the user the current state of management criteria and forecasts for the quality of final products.

IASU PV-3 has the following consumer characteristics:

• intuitive user interface for process personnel;
• software and information compatibility with ACS PV-3 and other information systems;
• the ability to adapt the system to other units at the level of filling the knowledge base without changing the software core of the system;
• localization of all user interface elements in Russian;
• reliability, openness, scalability, that is, the possibility of further expansion and modernization.

Monitoring and control of all units and actuators is carried out from the operator stations of the ACS PV-3, located in the control room PV-3.

In addition to existing operator stations, a specialized automated workstation is used, designed to provide the operator with a user interface of the IASU PV-3 system. Architecturally and functionally, IASU PV-3 looks like an addition to the existing ACS PV-3, that is, as an expansion of the functional and information functions of the existing control system.

IASU PV-3 provides real-time execution of the following application functions:

• assessment of the quantity and quality of the charge supplied to the furnace;
• forecast of the quality of final products;
• displaying the results of the operator’s decisions based on the criteria for the technological balance of the process;
• automatic analysis of the quality of process control;
• accumulation of a management knowledge base over the entire period of system operation;
• modeling of the PV-3 unit for use in the “Simulator” mode for the purpose of personnel training.

Architecture of IASU PV-3

IASU PV-3 is an expert system that implements intelligent monitoring and control of the melting process in operator advice mode. The control is implemented as a set of recommendations for the operator and senior smelter to maintain the technological balance of the process while achieving the set goals for the quality of the final smelting products, obtaining a given quantity of finished products (matte ladles) and melting of man-made materials.

The main elements of IASU PV-3, like any expert system, are: knowledge base; decision-making block; block for recognizing the input information flow (obtaining knowledge-based output). In Fig. Figure 3 shows the generalized architecture of the system.

The uniqueness of the methodology for extracting and presenting expert knowledge in the form of a nonlinear polynomial makes it possible to quickly synthesize a sufficient system of logical-linguistic models that systematically represent the features of technological processes. At the same time, the use of highly qualified specialists as experts who operate this particular unit with its characteristic features guarantees that the process occurring in it is carried out in accordance with the technological instructions of the enterprise.

The knowledge representation for describing Vanyukov’s process model is based on the “Semantic network on a basic ontology” representation. This representation involves the selection of a dictionary - a basic ontology based on an analysis of the subject area. Using the basic ontology and a set of features corresponding to the elements of the basic ontology, it is possible to build a semantic network that allows you to structure a complex multifactor model. Thanks to this description, on the one hand, a significant reduction in the dimension of the number of factors is achieved, and, on the other hand, the connections by which these factors are interconnected are unified. At the same time, the semantics and functionality of each of the factors under consideration are completely preserved.

All knowledge about the Vanyukov process and about the PV-3 unit in which this process is implemented is stored in the knowledge base (KB). The latter is designed as a relational data store and contains a formal record of knowledge in the form of records in tables.

The knowledge processor or decision-making unit as part of the expert system is implemented on the basis of the platform for the development of industrial expert systems G2 (Gensym, USA). The main elements of the knowledge processor (Fig. 3) are the following blocks: recognition of the input information flow; calculating the model for the current situation; situational analysis; decision making.

Let's take a closer look at these elements. At the moment the expert system is launched, the knowledge processor reads all the information from the knowledge base, which is recorded in the storage, and builds a model of the PV-3 unit and the Vanyukov process. Further, as the process and the PV-3 unit operate, data from the automatic control system of the unit is received into the automated control system system. These data characterize both the state of the process (specific oxygen consumption per ton of metal-containing materials, etc.) and the state of the PV-3 unit (the temperature of the exhaust water from the caissons of each row, the state of the tuyeres for supplying blast to the melt, etc.). The data enters the recognition block, is identified in terms of technological consistency criteria, and then, based on this data, a calculation is performed using the Vanyukov process model. The results of this calculation are analyzed in the situation analysis block and if a violation of the technological balance occurs, the situation is identified by the system as “conflict”. Next, a decision is made regarding the restoration of the technological balance. The resulting solutions, as well as information about the current state of the process, along with information about conflicts, are displayed in the client module of the IASU PV-3 (Fig. 4). The model is updated every minute.

Practical implementation

We will demonstrate the predictive capabilities of IASU PV-3 during its operation at the Copper Plant of the Polar Division of OJSC MMC Norilsk Nickel.

In Fig. Figure 4 shows the interface of the automated control system PV-3, the information of which serves as an addition to the operator to the main automated control system (Fig. 2) when making a control decision. Field 1 (Fig. 4) visualizes the calculation values ​​using the “Specific oxygen consumption per ton of metal containing” model. Reflection of the predictive ability of IASU PV-3 for the quality of the final product - copper content in the matte - is shown by the graph of field 2, and for silicon dioxide - field 3. The following indicators are displayed on the panel: 4 - copper content in the slag (%); 5 - percentage of fluxes in the load that contain metal; 6 - download quality (b/r); 7 - melt temperature (°C). Field 8 contains hourly calculated values ​​of the consumption of charge materials by bunkers, and field 9 reflects the names of the conflicts taking place at the current time. Increasing the accuracy of calculations using models is facilitated by switching to the appropriate control mode of the radio buttons of field 10. The fact of filling the converter slag is taken into account using the button of field 11.

Analysis of the minute-by-minute values ​​of the graph in field 1 shows the stable operation of the process within acceptable limits according to the criterion of specific oxygen consumption per ton of metal-containing materials, beyond which a loss of quality of the final products is guaranteed. Thus, being outside the designated boundaries for more than 10 minutes can lead to critical states of the process: below 150 m3/t - under-oxidation of the melt and, as a consequence, cold operation of the furnace; above 250 m3/t - overoxidation of the melt, and as a result, hot operation of the furnace.

The calculated copper content in matte based on actual data (field 2) clearly correlates with the behavior of the values ​​of the previous criterion (field 1).

Thus, in the time interval 17:49–18:03, the peaks on both graphs coincide, which reflects the fact of the system’s response to changes in the physico-chemical state of the PV: the routine operation of lance (cleaning) devices for supplying blast to the melt led to an increase in the specific oxygen consumption > 240 m3/t, caused a natural increase in the temperature of the melt and, thereby, caused a natural increase in the copper content in the matte.

In addition, conducting the process at a specific oxygen consumption in the region of 200 m3/t naturally determines the copper content in the matte of 57...59% during the observed 2-hour interval.

Comparing the behavior of the blue and green graphs (field 1) indicates that the operator follows the system’s recommendations almost all the time. At the same time, the real values ​​of the “Specific consumption” criterion differ from the recommended ones due to a) natural fluctuations in the readings of the sensors of the PV-3 unit in terms of blast flow; b) technological operation of furnace tuyere (peak on the graph); c) chemical changes in the state of the melt pool due to fluctuations in the composition of the raw material. Please note that according to the criterion “% of fluxes containing metals,” the operator works with excess consumption (yellow indicator zone 5) relative to the system recommendations. A similar situation is associated with the presence of technogenic raw materials in the load. As a result, fluctuations in the silicon dioxide content in the melt become difficult to predict, and the system warns the operator that prolonged operation in this flux loading mode can lead to a technological imbalance. The fact of the presence of man-made raw materials in the load is also confirmed by the calculated parameter “Load Quality” (indicator 6), which displays the value in the red zone - “Poor quality raw materials”.

Thus, the system guides the operator in conducting the process within a “narrow” range of values ​​of the main technological consistency parameters, while indicating the quality of the product that will be obtained as a result of melting.

Conducting the process within the specified boundaries of the main technological criteria also makes it possible to optimize the blast operating mode of the furnace, in particular, to reduce the consumption of natural gas in the blast.

Visualization of trends according to the main criteria also has a positive psychological impact on the process operator, since it “justifies” in quantitative form the implementation of the decision made when managing the process.8 9

Conclusion

Developed by the Summa Technologies company and tested at the Copper Plant of the Polar Division MMC Norilsk Nickel, the Intelligent Automated System for Monitoring and Control of the Vanyukov Process IASU PV-3 as a “complex technical system” allows us to make some generalizations in relation to the use of the results obtained in other fields of knowledge and industry.

The synthesis of the above independent technologies makes it possible to create an automated control system for almost any “complex technical system” in the presence of the customer’s existing basic automation and highly qualified specialists who operate such systems quite effectively under conditions of “significant uncertainty.”

The proposed approach to constructing an IAS has several other advantages. Firstly, it provides significant time savings due to the fact that the first technology (using an ontological approach) is already implemented in the software product and allows you to process knowledge about any models in the knowledge base, and the second (building a system of mathematical equations for a complex technological process) in Due to the recipe's developed method of application, it requires a minimum of appeals to an expert. Secondly, the use of expert knowledge in relation to assessing the technical condition of a particular object is carried out in the conditions of technological regulations for its operation, which minimizes the risk of the system making an incorrect decision, and real-time monitoring contributes to the early detection of approaching extreme (pre-emergency) process states. Thirdly, the most general approach to solving multi-level recognition of the technical state of complex technological processes, objects or phenomena in any industry has actually been implemented - non-ferrous and ferrous metallurgy, mining and oil and gas production, chemical industry, thermal power engineering, agriculture, etc.

Bibliography

1. Sokolov B.V., Yusupov R.M. Conceptual basis for assessing and analyzing the quality of models and multi-model complexes.//Izv. RAS. Theory and control systems. 2004. No. 6. P. 6–16.

2. Spesivtsev A.V. Metallurgical process as an object of study: new concepts, consistency, practice. - St. Petersburg: Polytechnic Publishing House. University, 2004. - 306 p.

3. Spesivtsev A.V., Lazarev V.I., Daymand I.N., Negrey D.S. Assessing the degree of consistency in the functioning of a technological process based on expert knowledge.//Sb. reports. XV International Conference on Soft Computing and SCM Measurements. St. Petersburg, 2012, T. 1. - pp. 81–86.

4. Okhtilev M.Yu., Sokolov B.V., Yusupov R.M. Intelligent technologies for monitoring and controlling the structural dynamics of complex technical objects. - M.: Nauka, 2006. - 410 p.

5. Narignani A.S. NON-factors and knowledge engineering: from naive formalization to natural pragmatics//KII 94. Collection of scientific works. works Rybinsk, 1994. - pp. 9–18.

6. Spesivtsev A.V., Domshenko N.G. Expert as an “intelligent measuring and diagnostic system”.//Sb. reports. XIII International Conference on Soft Computing and SCM Measurements. S.-Petersburg, 2010, T. 2. - P. 28–34.

7. Vanyukov A.V., Bystrov V.P., Vaskevich A.D. and others. Melting in a liquid bath / Ed. Vanyukova A.V.M.: Metallurgy, 1988. - 208 p.

Artificial intelligence(English – artificial intelligence) are artificial software systems created by man on a computer base and simulating the solution of complex creative problems by man in the process of his life. According to another similar definition, “artificial intelligence” is computer programs with the help of which a machine acquires the ability to solve non-trivial problems and ask non-trivial questions.”

There are two areas of work that make up artificial intelligence (AI). The first of these directions, which can be conventionally called bionic, aims to simulate the activity of the brain, its psychophysiological properties, in order to try to reproduce artificial intelligence (intelligence) on a computer or using special technical devices. The second (main) direction of work in the field of AI, sometimes called pragmatic, is associated with the creation of systems for automatically solving complex (creative) problems on a computer without taking into account the nature of the processes that occur in the human mind when solving these problems. Comparison is carried out based on the effectiveness of the result and the quality of the solutions obtained.

1) Exists target, i.e. the final result towards which a person’s thought processes are directed (“The goal makes a person think”).

2) The human brain stores a huge number facts And rules their use. To achieve a certain goal, you just need to turn to the necessary facts and rules.

3) Decision making is always carried out on the basis of a special simplification mechanism, which allows you to discard unnecessary (unimportant) facts and rules that are not related to the problem being solved at the moment, and, conversely, highlight the main, most significant facts and rules necessary to achieve the goal.

4) By achieving a goal, a person not only comes to a solution to the task assigned to him, but also at the same time acquires new knowledge.

Building a universal AI system covering all subject areas is impossible, since it would require an infinite number of facts and rules. More realistic is the task of creating AI systems that are designed to solve problems in a narrowly defined, specific problem area.

Rice. 5.1. AI System Components

Such systems, using the experience and practical knowledge of expert specialists in a given subject area, are called expert systems(expert systems).

The use of expert systems turns out to be extremely effective in a wide variety of areas of human activity (medicine, geology, electronics, petrochemistry, space research, etc.). This is explained by a number of reasons: firstly, it becomes possible to solve previously inaccessible, poorly formalized problems using a new mathematical apparatus specially developed for these purposes (semantic networks, frames, fuzzy logic, etc.); secondly, the expert systems being created are aimed at their operation by a wide range of specialists (end users), communication with whom takes place in an interactive mode, using reasoning techniques and terminology of a specific subject area that they understand; thirdly, the use of an expert system can dramatically increase the efficiency of decisions made by ordinary users due to the accumulation of knowledge in the expert system, including the knowledge of highly qualified experts.

An expert system includes a knowledge base and subsystems: communication, explanation, decision making, knowledge accumulation. The following are connected to the expert system through the communication subsystem: the end user; expert – a highly qualified specialist whose experience and knowledge far exceeds the knowledge and experience of an ordinary user; a knowledge engineer who is familiar with the principles of building an expert system and knows how to work with experts in this field, and is proficient in special languages ​​for describing knowledge.

Control systems built on the basis of expert regulators that simulate the actions of a human operator under conditions of uncertainty in the characteristics of the object and the external environment are called intellectual control systems (intelligent control systems).

According to another similar definition, intellectual A control system (MCS) is one that has the ability to understand, reason and study processes, disturbances and operating conditions. The factors being studied include mainly process characteristics (static and dynamic behavior, disturbance characteristics, equipment operating practices). It is desirable that the system itself accumulates this knowledge, purposefully using it to improve its quality characteristics.

Sources of financing investment activities. Analysis of the structure and dynamics of property and the sources of its formation. The main directions for increasing investment attractiveness: increasing the organization’s profit by expanding the sales market.

Send your good work in the knowledge base is simple. Use the form below

Students, graduate students, young scientists who use the knowledge base in their studies and work will be very grateful to you.

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Ministry of Education and Science of the Russian Federation

Federal State Budgetary Educational Institution

higher education

TOMSK STATE UNIVERSITY OF CONTROL SYSTEMS AND RADIO ELECTRONICS (TUSUR)

Department of Economics

Assessing the investment attractiveness of an organization (using the example of Synthesis of Intelligent Systems LLC)

Bachelor's work

in the direction of 38.03.01 - Economics profile “Finance and Credit”

Final qualifying work 73 pages, 5 figures, 16 tables, 23 sources.

Object of study - Limited Liability Company "Synthesis of Intelligent Systems".

The purpose of the work is to assess the investment attractiveness of the organization SIS LLC and offer recommendations for its improvement.

To achieve this goal, the following tasks were solved:

The theory of investment attractiveness is analyzed, the essence of the concept of investment and its classification, the concept of investment attractiveness are determined;

Methods for assessing the investment attractiveness of an organization are analyzed;

An assessment of the investment attractiveness of the organization SIS LLC was carried out based on financial and economic indicators;

The main directions for increasing investment attractiveness are proposed, namely: increasing the organization’s profit by expanding the sales market.

The information base of the research, as part of the implementation of this final qualifying work, consisted of: data from the accounting statements of the enterprise, information posted on the official website of the organization, research materials of scientists published in scientific journals, scientific articles in periodicals, teaching aids, as well as information resources of the network Internet.

Final qualification work 73 pages, 5 drawings, 16 tables, 23 sources.

The object of the research is the company Limited Liability Company “Synthesis of intelligent systems”

The purpose of the work is assess the investment attractiveness of the organization SIS LLC and propose recommendations for improving it.

To achieve this goal, the following tasks were accomplished:

The theory of investment attractiveness is analyzed, the essence of the concept of investments and their classification, the concept of investment attractiveness are defined;

Methods for evaluating the investment attractiveness of the organization are analyzed;

An assessment of the investment attractiveness of the organization "SIS" on the basis of financial and economic indicators;

The main directions of increasing the investment attractiveness are proposed, namely: increase of the profit of the organization due to the expansion of the sales market.

Information base of the research, within the framework of this final qualifying work, was: data of the enterprise"s accounting reports, information posted on the official website of the organization, research materials of scientists published in scientific journals, scientific articles in periodicals, teaching aids, and information resources of the network The Internet.

INTRODUCTION

In modern conditions, organizations of various forms of ownership are tasked with increasing their productivity, competitiveness, profitability and financial independence in the long term, which directly depends on the existing level of investment activity of the organization, the scope of its investment activities and investment attractiveness.

Investment attractiveness is an indicator by which investors make decisions about investing their funds in a particular organization.

The relevance of the chosen topic is due to the fact that potential investors, as well as managers, need to have a clear model for assessing the investment attractiveness of an organization for the most effective management or making an investment decision. Also, the level of investment attractiveness is important for creditors and customers, the former are interested in the creditworthiness of the organization, and the latter are interested in the reliability of business relations, continuity and stability of the organization’s activities, which depend on the liquidity and state of the financial stability of the organization.

Set of indicators selected for evaluation

investment attractiveness depends on the specific goals of the investor.

The significance of determining the investment attractiveness of organizations is beyond doubt, since without this, investments will not be made in business entities and, as a result, economic growth and its stabilization will not be possible. In some cases, investments ensure the viability of the organization as a whole.

Financial analysis as the main mechanism that ensures the financial stability of an organization and assesses its attractiveness for potential investors is the central link in the methodology for determining investment attractiveness. Its main goal is to study the problems that arise when assessing the financial attractiveness of an organization for an investor. In this regard, aspects of the analysis of the financial condition of the organization are considered, the level of profitability, creditworthiness, efficiency and financial stability is assessed.

The result of the financial analysis is the identification of the main directions for increasing the investment attractiveness of the analyzed organization.

The purpose of the thesis is to study theoretical aspects related to the concept of investment attractiveness and methods for assessing it, directly assessing investment attractiveness using the example of the organization Synthesis of Intelligent Systems LLC, as well as developing recommendations for improving the investment attractiveness of the organization.

To achieve this goal, it is necessary to solve the following tasks:

Determine the essence and classify investments;

Study methods for assessing the investment attractiveness of an organization;

Assess the investment attractiveness of the organization based on the chosen methodology;

The object of the study is the organization Synthesis of Intelligent Systems LLC.

1. THEORETICAL BASIS OF INVESTMENT ACTIVITIES OF AN ORGANIZATION

1.1 Nature and classification of investments

There is no common understanding of the essence of investment as an economic category among scientists and economists. There are different interpretations that differ in meaning, some of which do not convey the full essence of this term.

According to the federal law of February 25, 1999 N 39-FZ “On investment activities in the Russian Federation, carried out in the form of capital investments” “... investments - cash, securities, other property, including property rights, other rights that have a monetary value , invested in objects of entrepreneurial and (or) other activities in order to make a profit and (or) achieve another useful effect.”

Based on the versatility of interpretations of the term, we can distinguish the economic and financial definition of investment. The economic definition characterizes investments as a set of costs realized in the form of long-term capital investments in various sectors of the economy of the production and non-production spheres. From a financial point of view, investments are all types of resources invested in business activities with the aim of generating income or benefit in the future.

In general, investments mean the investment of capital in all its forms with the aim of generating income in the future or solving certain problems.

An organization may or may not carry out investment activities, but failure to carry out such activities leads to a loss of competitive position in the market. It follows from this that investments can be passive and active:

passive - investments that ensure, at a minimum, no deterioration in the profitability of investments in the operations of a given organization by replacing outdated equipment, training new personnel to replace retired employees, etc.

active - investments that ensure an increase in the competitiveness of the company and its profitability in comparison with previous periods through the introduction of new technologies, the release of goods that will be in high demand, the capture of new markets, or the absorption of competing firms.

Investments are divided into the following groups:

By investment objects:

1) real investments are investments in fixed capital in various forms (purchase of patents, construction of buildings, structures, investments in scientific developments, etc.);

2) financial (portfolio) investments are investments in shares, bonds and other securities that give the right to receive income from property, as well as bank deposits.

By the nature of participation in investment:

1) direct investments are investments made by direct investors, i.e. legal entities and individuals who fully own the organization or a controlling stake, which gives the right to participate in the management of the organization;

2) indirect investments are investments made through financial intermediaries (investment consultants, financial brokers; brokerage houses; mutual funds; commercial banks; insurance companies).

By investment period:

short-term investments - investments of capital for a period from a week to one year. These investments are usually speculative in nature. The main task of a short-term investor is to calculate the direction of movement of a security on a scale of weeks and months, to determine the entry point with the highest ratio of potential income to risk;

medium-term investments - investing funds for a period of one to five years;

long-term investments - investments of 5 years or more (capital investments in the reproduction of fixed assets).

By type of ownership of investment resources:

public investments - carried out by public authorities and management at the expense of budgets and extra-budgetary funds;

private investments - investments made by individuals or legal entities with the aim of generating income in the future;

combined investments - investments of funds made by entities of a given country and foreign countries in order to obtain a certain income;

foreign investment - investment of capital by foreign investors in order to make a profit.

Chronologically:

initial investments - aimed at creating an enterprise or constructing a new facility;

current investments - aimed at maintaining the level of technical equipment of the facility.

By investment goals:

for replacement of fixed capital;

to expand production;

to purchase securities of other organizations;

on innovative technologies.

By level of investment risk:

low-risk investments;

medium-risk investments;

high-risk investments.

By level of investment attractiveness:

low-attractive;

moderately attractive;

highly attractive.

Individuals or legal entities who place capital on their own behalf and at their own expense for the purpose of making a profit are called investors.

Investors can invest their own, borrowed and borrowed funds. Investors can be bodies authorized to manage state and municipal property or property rights, legal entities of all forms of ownership, international organizations and foreign legal entities, individuals.

Sources of financing investment activities are:

The organization’s own financial resources and internal reserves (profit, depreciation, cash savings and savings of citizens and legal entities, funds paid by insurance authorities in the form of compensation for losses from accidents, natural disasters, etc.);

Raised financial resources (received from the sale of shares, shares and other contributions from members of labor collectives, citizens, legal entities);

Borrowed financial resources or transferred funds (bank and budget loans, bond issues, etc.);

Funds from extra-budgetary funds;

Federal budget funds provided on a non-refundable basis, funds from the budgets of the constituent entities of the Russian Federation;

Funds from foreign investors.

Investments can be received from either one or several sources. There are centralized (budgetary) - funds from the federal budget, funds from the budgets of constituent entities of the Russian Federation and local budgets - and decentralized (extra-budgetary) - own funds of enterprises and organizations, foreign investments, borrowed funds, funds from extra-budgetary funds - sources of investment.

1.2 Investment attractiveness of the organization and methods for its assessment

The works of many scientists are devoted to the study of the concept of investment attractiveness and methods for its assessment, for example, I.A. Blanca, V.V. Bocharova, E.I. Krylov and others.

Each scientist interprets the concept of investment attractiveness depending on the factors included in its assessment, i.e. there is no single interpretation. There are many factors that influence investment attractiveness, therefore, in a narrow sense, investment attractiveness is a system or combination of various features or factors of the internal and external environment.

The most clearly different points of view on understanding investment attractiveness are reflected in Table 2.1.

Table 2.1 - Interpretation of the concept of “investment attractiveness”

	Interpretation of the concept
Blank I.A., Kreinina M.N.	A general description of the advantages and disadvantages of investing in individual areas and objects from the position of a specific investor.
Roizman I.I., Shakhnazarov A.G., Grishina I.V.	A system or combination of various objective features, means, opportunities that together determine the effective demand for investment in a country, region, industry, enterprise.
Sevryugin Yu.V.	A system of quantitative and qualitative factors characterizing the effective demand of an enterprise for investment.
Lyakh P.A., Novikova I.N.	A set of characteristics of the most profitable and least risky investment of capital in any area of ​​the economy or in any type of activity.
Tryasitsina N.Yu.	A set of indicators of an enterprise’s performance that determines the most preferable values ​​of investment behavior for an investor.
Ministry of Economic Development Group	The volume of investment that can be attracted based on the investment potential of the object, risks and the state of the external environment.
Putyatina L.M., Vanchugov M.Yu.	An economic category that characterizes the efficiency of using the enterprise’s property, its solvency, financial stability, ability for innovative development based on increasing the return on capital, the technical and economic level of production, the quality and competitiveness of products.
Igolnikov G.L., Patrusheva E.G.	Guaranteed, reliable and timely achievement of the investor’s goals based on the economic results of the investment production.
Guskova T.N., Ryabtsev V.M., Geniatulin V.N.	A certain state of economic development in which, with a high degree of probability, within a timeframe acceptable to the investor, investments can provide a satisfactory level of profit or a positive effect can be achieved.
Krylov E.I.	A generalized description from the point of view of prospects, profitability, efficiency and minimizing the risk of investing in the development of an enterprise at the expense of one’s own funds and the funds of other investors.
Modorskaya G.G.	A set of economic and psychological indicators of an enterprise’s activity that determine for an investor the area of ​​preferred values ​​of investment behavior.
Bocharov V.V.	Availability of economic effect (income) from investing money with a minimum level of risk.
Sharp W., Markowitz H.	Obtaining maximum profit at a given level of risk.
Eriyazov R.A.	A complex category that includes taking into account internal factors in the form of investment potential, external factors - the investment climate and the contradictory unity of objective and subjective factors in the form of taking into account the level of risk and profitability of investment activity with the coordination of the interests of the investor and the recipient.
Latsinnikov V.A.	An indicator of its total value, which is a set of objective (financial condition of the enterprise, level of its development, quality of management, burden of debt) and subjective (ratio of profitability and risk of investments) characteristics necessary to satisfy the interests of all participants in the investment process, allowing to assess the feasibility and prospects of investments and taking into account the combined influence of macro- and meso-environmental factors
Nikitina V.A.	The economic feasibility of investing, based on the coordination of the interests and capabilities of the investor and the recipient of investments, which ensures the achievement of the goals of each of them at an acceptable level of profitability and risk
Ivanov A.P., Sakharova I.V., Khrustalev E.Yu.	A set of economic and financial indicators of an enterprise that determine the possibility of obtaining maximum profit as a result of investing capital with minimal investment risk.

In this work, investment attractiveness will be presented as a set of indicators of an organization’s performance that reflect the development of the organization over time, as well as the rational use of available resources.

Investment attractiveness is considered at various levels: at the macro level - the investment attractiveness of the country, at the meso level - the investment attractiveness of the region and industry, at the micro level - the investment attractiveness of the organization.

There are a large number of options for assessing investment attractiveness, this is due to the fact that there is no specific definition of the term “investment attractiveness.” Of all of them, the following methods can be noted, based on the factors included in the assessment methodology:

based on the relationship between profitability and risk (W. Sharp, S.G. Shmatko, V.V. Bocharov) - establishing the company’s investment risk group. Consequently, an analysis of the risks arising during investment activities is carried out, the significance of the risk is established, and the overall investment risk is calculated. Next, the organization’s belonging to a certain risk category is determined, on the basis of which the investment attractiveness is determined. Key risks considered: the risk of decreased profits, the risk of loss of liquidity, the risk of increased competition, the risk of changes in the pricing policy of suppliers, etc.

based solely on financial indicators (M.N. Kreinina, V.M. Anshin, A.G. Gilyarovskaya, L.V. Minko) - an analysis of the financial condition is carried out by calculating financial ratios that reflect different aspects of the organization’s activities: property status, liquidity, financial strength, business activity and profitability. For the assessment, data from the organization’s financial statements is used.

based on financial and economic analysis, in which not only financial but also production indicators are calculated (V.M. Vlasova, E.I. Krylov, M.G. Egorova, V.A. Moskvitin) - production indicators appear that reflect the availability of fixed assets, the degree of their wear and tear, the level of capacity utilization, the availability of resources, the number and structure of personnel and other indicators.

based on a comprehensive comparative assessment (G.L. Igolnikov, N.Yu. Milyaev, E.V. Belyaev) - an analysis of indicators of the financial condition, market position of the organization, development dynamics, personnel qualifications, and level of management is carried out. When using this method, groups of factors are first determined at different levels: country, region, organization, then these groups are selected by significance based on expert assessments. The significance coefficients of each individual factor in the group of factors are also determined, then all factors are summarized taking into account the influence of the significance of each group and factor in the group. The obtained data is ranked and the most investment-attractive organizations are determined. Factors influencing the investment attractiveness of a country are: the discount rate and its dynamics, inflation rates, technological progress, the state of the country's economy, the level of development of the investment market. Indicators for assessing the investment attractiveness of a region are: production and economic indicators (price index, product profitability, capital productivity, share of all material costs, number of operating organizations), financial indicators (liquidity ratios, autonomy ratios, etc.), industry production factors (the level of production capacity utilization, the degree of depreciation of fixed production assets), indicators of investment activity of the industry (the number of investments per organization, the number of investments per employee, the index of the physical volume of investments in fixed capital, etc.).

based on the cost approach, which is based on determining the market value of the company and the tendency to maximize it (A.G. Babenko, S.V. Nekhaenko, N.N. Petukhova, N.V. Smirnova) - the coefficient of undervaluation/overvaluation of the organization is calculated by the market of real investments as the ratio of various values ​​(real value to market value). Real value is defined as the sum of the value of the property complex and discounted income minus accounts payable. Market value is the most possible price for a transaction in a certain period of time, based on market conditions.

These methods are designed for strategic investors whose goal is long-term investment of funds, which involves managing the organization and its operational activities to achieve specific objectives, and most importantly, to increase the value of the organization. Investors who place their investments for a short period of time (speculators) usually use the theory of portfolio investments (a method of forming an investment portfolio aimed at the optimal selection of assets based on the required return/risk ratio), fundamental (price forecasting using financial indicators) to assess the investment attractiveness activities of the company and calculation of the internal value of the company) and technical analyzes (forecasting future value using charts and indicators).

Financial attractiveness is identified as the main component of investment attractiveness, since the organization’s finances reflect the main results of its activities. Based on this, the analysis of the investment attractiveness of the analyzed organization will be carried out according to the methodology of financial and economic analysis, namely on the basis of indicators for assessing the financial condition, which includes:

analysis of the structure and dynamics of property;

analysis of the structure and dynamics of profit;

balance sheet liquidity analysis;

solvency analysis;

credit analysis;

business activity analysis:

6.1) analysis of turnover;

6.2) analysis of return on capital.

financial stability analysis;

bankruptcy probability analysis.

External and internal factors of investment attractiveness will also be considered, such as the investment attractiveness of the region and industry, the organizational and managerial structure of the organization, and market coverage.

2. ASSESSMENT OF INVESTMENT ATTRACTIVENESS OF "SYNTHESIS OF INTELLIGENT SYSTEMS" LLC

2.1 Brief description of the organization LLC "SIS"

Limited Liability Company "Synthesis of Intelligent Systems" belongs to IT organizations and specializes in the development of websites and mobile applications. The organization was created in 2015 based on the minutes of the meeting of founders, and is currently located in Tomsk.

The goal of creating Synthesis of Intelligent Systems LLC was to obtain maximum profits at minimum costs by providing software development services.

The range of services provided by Synthesis of Intelligent Systems LLC:

website development from scratch on the 1C-Bitrix platform;

website development using a template on the 1C-Bitrix platform;

technical maintenance of finished websites;

completion and improvement of finished sites;

mobile application development;

sale of licenses to 1C-Bitrix LLC.

The main clients are legal entities and individual entrepreneurs, there are orders from government agencies.

According to the current classification, the analyzed organization can be classified as a small business, since its average headcount at the beginning of 2017 was 17 people, and the authorized capital is entirely owned by private individuals.

Due to not exceeding revenue in the amount of 112.5 million rubles for the nine months of last year, not exceeding the average number of employees for 2015 in the amount of 100 people, the residual value of fixed assets - 150 million rubles, the organization applies a simplified system taxation with the object of taxation being income minus expenses with an interest rate of 7% provided for IT organizations. In accordance with clause 85 of the “Regulations on accounting and financial reporting in the Russian Federation”, approved by order of the Ministry of Finance of the Russian Federation dated July 29, 1998 No. 34n, small enterprises have the right to prepare financial statements in a reduced volume (balance sheet and financial performance statement). SIS LLC applies this right in full.

2.2 Assessing the investment attractiveness of an organization

investment market sales profit

Analysis of the structure and dynamics of property and sources of its formation

The first stage of the assessment is to conduct a vertical (structural) and horizontal (temporal) analysis.

Horizontal analysis is aimed at studying the growth rate of indicators, which explains the reasons for changes in their structure, thus it represents the absolute and relative change in indicators over a period. Vertical analysis is an analysis of the structure in comparison with the previous period, it helps to understand which indicators had the most significant impact on the indicators.

An analysis of the dynamics and structure of the organization’s property and the sources of its formation is presented in Table 3.1.

Table 3.1 - Analysis of the dynamics and structure of the organization’s property and the sources of its formation

The name of indicators	Absolute values	Relative values	Changes
	2015, thousand rubles	2016, thousand rubles			In absolute terms, thousand rubles.	In structure, %	Rate of increase
Tangible non-current assets
Intangible, financial and other non-current assets
Cash and cash equivalents
Financial and other current assets (including accounts receivable)
Capital and reserves
Long-term borrowed funds
Other long-term liabilities
Short-term borrowed funds
Accounts payable
Other current liabilities

Conclusions obtained from the analysis of the balance sheet asset:

The balance sheet assets are dominated by financial and other current assets of the organization, and in this case, entirely consisting of receivables, which make up 64% of the balance sheet currency. The shares of other assets are insignificant. The share of tangible non-current assets, namely fixed assets, decreased by 23%, probably due to wear and tear of capital equipment. In absolute terms, fixed assets decreased by 78 thousand rubles, which is likely due to the disposal of fixed assets in the current period. The share of intangible, financial and other non-current assets, namely acquired licenses, decreased by 4%, which indicates the abandonment of minor software. The share of cash and cash equivalents increased by 5%, in cash equivalent by 238 thousand rubles, which is associated with an increase in the volume of services provided. In connection with the increase in volumes, the share of financial and other current assets, represented in this case exclusively by accounts receivable, increased by 22%, which is the provision of deferred payments to customers, as well as the unstable solvency of the bulk of customers.

The growth rate of the balance sheet currency was 131%, which indicates the development of the organization, but since the growth was mainly due to an increase in accounts receivable, although it is an indicator of an increase in the volume of services provided, in general it is a negative indicator - a withdrawal of funds from the organization’s turnover.

Conclusions obtained from the analysis of sources of property formation:

The structure of the balance sheet liability is dominated by accounts payable, amounting to 74%, the growth rate of which was 1192%. An increase in accounts payable shows the organization's inability to pay current obligations. In the reporting period, the amount of accounts payable amounted to 1,550 thousand rubles. The share of other long-term liabilities, representing loans from the founders, decreased significantly by 36%, in monetary terms by 201 thousand rubles, directly related to the repayment of loans. Short-term borrowed funds and other short-term obligations that were necessary when opening the organization were fully repaid by 10% and 2%, respectively, which positively characterizes the organization that is able to pay off short-term obligations. The share of long-term borrowed funds decreased by 12%, which shows that the organization After paying off short-term obligations, it began to liquidate long-term debt. The share of equity capital, which represents the authorized capital, has not changed and in monetary terms is 15 thousand rubles. In the overall structure of the balance sheet, the share of equity is less than 1%, which undoubtedly characterizes the unstable financial position of the organization.

The dynamics of the structure of assets and liabilities of the balance sheet is clearly shown in Figure 3.1.

Figure 3.1 - Dynamics of structural assets and liabilities for 2015-2016

Analysis of the structure and dynamics of performance results

When analyzing performance results, vertical and horizontal analysis is also carried out. The results of the analysis show from which indicators profit is formed, the dynamics of indicators and their impact on the organization’s net profit. An analysis of the dynamics and structure of profit is shown in Table 3.2.

Table 3.2. - Analysis of the dynamics and structure of profit

Name indicators			Deviation	revenue in Last year	in % of revenue in the reporting	Deviation
Expenses for ordinary activities
Percentage to be paid
Other income
other expenses
Profit taxes (income)
Net income (loss)

Conclusion from the analysis: The most significant impact on profit is exerted by expenses for ordinary activities, which increased in 2016 by RUB 3,937 thousand. In 2016, other expenses appeared, the amount of which amounted to 73 thousand rubles. and includes the cost of maintaining a bank account. Revenue in 2016 increased by 4,731 thousand rubles. and amounted to 7535 thousand rubles, which characterizes business development. Accordingly, net profit also increased in 2016 by 721 thousand rubles. and amounted to 1100 thousand rubles.

The dynamics of profit indicators are presented in Figure 3.2.

Figure 3.2 - Dynamics of profit indicators

Balance sheet liquidity analysis

Liquidity of an organization is an economic term that refers to the ability of assets to be quickly sold at a price close to the market price.

Depending on the degree of liquidity, the organization’s assets are divided into the following groups:

A1 = most liquid assets = cash + short-term financial investments

A2 = quick-selling assets = accounts receivable

A3 = slowly selling assets = inventories + long-term receivables + VAT + other current assets

A4 = hard-to-sell assets = non-current assets

Balance sheet liabilities are grouped according to the degree of urgency of payment:

P1= most urgent obligations = accounts payable

P2= short-term liabilities = short-term loans and credits + debts to participants for payment of income + other short-term liabilities

P3 = long-term liabilities = long-term liabilities + deferred income + reserves for future expenses

P4 = permanent \ stable liabilities = capital and reserves

The balance is considered absolutely liquid if the following ratios exist:

A1>P1; A2>P2; A3 > P3; A4< П4.

A comparison of these groups of assets and liabilities is presented in Table 3.3.

Table 3.3 - Comparative analysis of the organization’s assets and liabilities

Based on the comparative analysis, the following conclusions can be drawn:

the organization cannot pay off its most urgent obligations with absolutely liquid assets;

the organization cannot repay long-term loans with slowly selling assets;

the organization does not have a high degree of solvency and cannot pay off various types of obligations with the corresponding assets.

Since the ratios are not met, the balance is considered illiquid, i.e. the organization is unable to pay its obligations.

Solvency analysis

The solvency of an organization is the ability of an economic entity to repay its accounts payable in full and on time. Solvency is one of the key signs of a sustainable financial position of an organization.

The solvency of an organization from the perspective of asset liquidity is analyzed using special financial ratios - liquidity ratios:

general liquidity indicator - shows the organization’s ability to pay off its obligations in full with all types of assets;

absolute liquidity ratio; reflects the organization’s ability to pay off its short-term obligations using highly liquid assets. (calculated as the ratio of cash and short-term financial investments to short-term liabilities);

quick liquidity ratio - shows the possibility of repaying short-term liabilities with the help of quickly liquid and highly liquid assets (calculated as the ratio of highly liquid current assets to short-term liabilities);

current liquidity ratio - reflects the organization’s ability to pay off its current obligations using current assets. (calculated as the ratio of current assets to short-term liabilities);

coefficient of maneuverability of operating capital; The agility ratio shows what part of the operating capital is immobilized in inventories and long-term receivables;

the share of working capital in the asset - characterizes the presence of working capital in the assets of the organization;

equity ratio - reflects the degree to which the organization uses its own working capital; shows the share of the company's current assets financed from the organization's own funds.

The calculation of solvency indicators is presented in Table 3.4.

Table 3.4 - Analysis of the organization's solvency

Indicators	Symbol		Indicator value	Change
General liquidity ratio		(A1+0.5A2+0.3A3)/(P1+0.5P2+0.3P3);
Absolute liquidity ratio
Quick ratio		(A1 + A2) / (P1 + P2)
Current ratio		(A1 + A2 + A3) / (P1 + P2)
Operating capital maneuverability ratio		A3 /((A1 + A2 + A3) - (P1 + P2))	decrease in indicator
Share of working capital in assets		(A1+A2+A3) / Balance total
Own funds ratio		(P4 - A4) / (A1 + A2 + A3)

Conclusion from the analysis: The overall liquidity indicator in 2016 decreased and amounted to 0.59, which shows a non-optimal level of liquidity of the organization. The absolute liquidity ratio decreased by 0.32 and amounted to 0.16, which indicates that the amount of cash can cover only 16% of the company’s liabilities, which is not enough to maintain the normal level of liquidity of the organization. The quick liquidity ratio was 1.07, which is slightly higher than the norm and indicates the possibility of quickly repaying debts in the medium term. This means that SIS LLC is able to withdraw funds from circulation at an average speed and pay off short-term obligations. The current liquidity ratio was 1.07 in 2016, which indicates low solvency. The functional agility coefficient has a zero value due to the organization’s lack of slow-selling assets. The share of working capital increased by 0.27 and amounted to 0.8, which is a positive factor and shows an increase in balance sheet liquidity. The security ratio has a negative value, but is positive in dynamics; in 2016 it was -0.25, which shows that current assets are financed by borrowed funds of the organization, since the value of the coefficient is less than 0.1 and the current liquidity ratio is less than 2, then the organization is insolvent.

Credit analysis

The concept of an organization's solvency is closely related to its creditworthiness. Creditworthiness reflects to a greater extent the repayment of obligations using the medium-term and short-term assets of the organization, excluding permanent assets.

The main indicators of solvency are:

the ratio of sales volume to net current assets;

Net current assets are the current assets minus the organization's short-term debts. The ratio of sales volume to net current assets shows the efficiency of using current assets.

ratio of sales volume to equity capital;

short-term debt to equity ratio;

ratio of accounts receivable to sales revenue.

The calculation of creditworthiness indicators is presented in table 3.5.

Table 3.5 - Analysis of creditworthiness indicators

Indicators			Absolute deviation
Current assets, thousand rubles.
Short-term borrowed funds thousand rubles.
Revenue thousand rubles
Own capital thousand rubles.
Accounts receivable thousand rubles
Net current assets thousand rubles.
Indicators:
Ratio of sales volume to net current assets
Ratio of sales volume to equity capital
Short-term debt to equity ratio
Ratio of accounts receivable to sales revenue

Based on the analysis, the following conclusions can be drawn: The efficiency ratio of the use of current assets in 2016 compared to 2015 increased by 53.92, which shows the efficiency of the use of current assets. The ratio of sales volume to equity capital was 502.33, which was the result of a sharp increase in revenue. The ratio of short-term debt to equity increased by 88.53 and amounted to 103.33, which shows a high share of short-term debt in equity and the organization’s inability to pay its obligations. The accounts receivable to sales ratio increased by 0.04 to 0.18, which can be seen as a sign of lower creditworthiness as customer debts are converted into cash more slowly.

Analysis of business activity indicators

The next step is to analyze business activity indicators.

Analysis of business activity allows us to draw a conclusion about the effectiveness of the organization. Indicators of business activity are related to the speed of turnover of funds: the faster the turnover, the fewer semi-fixed expenses for each turnover, which means the higher the financial efficiency of the organization.

Analysis of business activity, as a rule, is carried out at two levels: qualitative (breadth of markets, business reputation of the organization and its clients, competitiveness, etc.) and quantitative indicators. In this case, the analysis of quantitative indicators consists of two stages: analysis of turnover (equity capital, current assets, accounts receivable and payable) and profitability.

Asset turnover analysis

Key turnover indicators include:

return on equity capital ratio - shows how many rubles. revenue accounts for 1 rub. average amount of invested equity capital;

capital productivity of fixed assets - characterizes the amount of revenue from sales per ruble of fixed assets;

return coefficient of intangible assets - reflects the efficiency of using intangible assets. It shows the amount of sales revenue in rubles per 1 ruble of the average amount of intangible assets, as well as the number of turnovers for the period;

total asset turnover ratio - shows how many monetary units of sold products each monetary unit of assets brought;

turnover ratio of current assets (current assets) - reflects the efficiency of use of current assets. It shows the amount of sales revenue in rubles per 1 ruble of the average amount of current assets, as well as the number of turnovers for the period;

cash turnover ratio - shows the period of cash turnover;

inventory turnover ratio - shows how many times during the period under study the organization used the average available inventory balance;

accounts receivable turnover ratio - shows the number of payments received from customers for the period in the amount of the average value of accounts receivable. Receivables repayment period - shows how many days on average the organization’s receivables are repaid;

accounts payable turnover ratio - shows how many times the company has repaid the average amount of its accounts payable. Repayment period of accounts payable - shows the average period for repayment of the organization’s debts for current obligations;

the operating cycle reflects the period of time from the moment materials are received at the warehouse until the moment when payment for the products is received from the buyer;

The financial cycle shows the length of time from the moment of payment for materials to suppliers and ending with the receipt of money from buyers for the delivered products.

The calculation of turnover indicators is presented in Table 3.6.

Table 3.6 - Turnover analysis

Indicators	Conditional designation	Calculation algorithm			Change
Continuation of Table 3.6
Number of days in the reporting year
Average cost of equity capital, thousand rubles.		(SKng+SKkg)/2
Average cost of fixed assets, thousand rubles.		(Osng+Oskg)/2
Average cost of intangible assets, thousand rubles.		(Nmang+Nmakg)/2
Average creditor debt, thousand rubles		(KZng+KZkg)/2
average cost assets, thousand rubles		(Ang+Akg)/2
Average cost of working capital assets, thousand rubles		(Aobng+ Aobkg)/2
Including:
Cash, thousand rubles		(DSng+DSkg)/2
Inventories, thousand rubles		(Zng+Zkg)/2
Accounts receivable, thousand rubles.		(DZng+DZkg)/2
Calculated odds:
Return on equity capital ratio
Return on assets
Return coefficient of intangible assets
Coefficient asset turnover
Coefficient turnover of current assets
Coefficient inventory turnover
Coefficient accounts payable turnover
Duration of turnover, days:
Current assets
Money
Accounts receivable
Accounts payable		D/kobcredit
Duration operating cycle		Ext. zap + Add. Deb
Duration financial cycle		D. pr.ts. + Add.deb-Add. Cred

Based on the data, the following conclusions can be drawn: The total asset turnover ratio in 2016 compared to 2015 decreased by 1.18, which shows a decrease in the efficiency of using all available resources, regardless of the sources of their financing (for each ruble of assets there are 5.04 rubles of products sold). The working capital turnover ratio in 2016 decreased by 4.75, which indicates a decrease in the efficiency of using current assets in the organization (for every ruble of current assets there are 7.04 rubles of sold products). The return ratio of intangible assets increased by 0.64, which shows the efficiency of using intangible assets (for every ruble of current assets there are 49.41 rubles of sold products). Capital productivity in 2016 increased by 9.63, which is evidence of better use of fixed production assets (for every ruble of current assets there are 27.60 rubles of sold products). The return on equity ratio increased by 128.47, which was achieved by increasing sales revenue, also due to the large share of profits obtained through the use of borrowed funds, which in the long term can negatively affect financial stability. The inventory turnover ratio is not calculated due to their absence. The cash turnover ratio increased by 4 days, which indicates the rational organization of the company’s work. The accounts receivable turnover ratio decreased by 6.07 and, accordingly, the turnover period increased by 17 days, which indicates a slower repayment of accounts receivable. The accounts payable turnover ratio decreased by 37.71 and, accordingly, the turnover period increased by 33 days, which indicates a slowdown in the repayment of accounts payable.

The duration of the operating cycle increased by 17 days, which is associated with an increase in the turnover period of receivables, i.e. the number of days required to transform raw materials into cash became 41 days.

The duration of the financial cycle decreased by 16 days, due to an increase in the duration of the turnover period of receivables and payables, i.e. the number of days between the repayment of accounts payable and accounts receivable is 1 day.

Cost-benefit analysis

In the broadest sense of the word, the concept of profitability means profitability, profitability. An organization is considered profitable if the results from the sale of products cover production costs and, in addition, generate an amount of profit sufficient for the normal functioning of the organization.

The economic essence of profitability can be revealed only through the characteristics of the system of indicators. Their general meaning is to determine the amount of profit from one ruble of invested capital.

The main profitability indicators are:

return on assets (economic profitability) - shows the amount of net profit per each monetary unit invested in the company's assets, reflects the efficiency of using the organization's assets.

2) return on equity - shows the amount of net profit for each cost unit of capital owned by the owners of the company.

3) return on sales - shows the amount of net profit of the organization from each ruble of products sold.

4) profitability of production - shows the amount of profit of the organization from each ruble spent on the production and sale of products.

5) return on invested capital - shows the ratio of profit to investments aimed at obtaining this profit. Investments are considered as the sum of equity and long-term debt.

The calculation of return on capital indicators is presented in table 3.7.

Table 3.7 - Return on equity analysis

Indicators	Conditional designation	Calculation algorithm			Absolute change
Revenue (net) from the sale of goods, products, works, services, thousand rubles.
Cost of sales of goods, products, works, services (including commercial and administrative expenses), thousand rubles.
Profit from sales, thousand rubles.
Net profit, thousand rubles.
Asset value, thousand rubles.		(Ang+Akg)/2
Own capital, thousand rubles.		(Skng+SKkg)/2
Long-term liabilities, thousand rubles.		(Dong+Dokg)/2
Profitability indicators:
Return on assets
Return on equity
Return on invested capital		PE/ (sk+Do)
Return on sales
Profitability of production

Return on sales in 2016 was 0.15, i.e. Each ruble of revenue received contained 15 kopecks of net profit, this figure increased by 0.01, which indicates a slight increase in demand for the services provided. Production profitability in 2016 was 0.18, i.e. Every ruble spent on services began to bring a net profit of 18 kopecks. Return on assets in 2016 decreased by 0.1 and amounted to 0.74, i.e. Each ruble of assets began to generate a profit of 74 kopecks. Return on equity increased by 23.47 and amounted to 74, which is associated with an increase in profits and an increase in debt capital. Return on invested capital increased by 0.7 and amounted to 1.87, i.e. Each ruble of investment began to generate a profit of 1.87 rubles.

Financial stability analysis

Financial stability is the ability of an organization to maintain its existence and uninterrupted operation, thanks to the availability of certain available funds and balanced financial flows. Financial sustainability means that an organization will be solvent in the long term.

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