Ensuring the required quality indicators for the functioning of large systems, associated with the need to study the flow of stochastic processes in the investigated and projected systems, 5 makes it possible to carry out a complex of theoretical and experimental studies mutually complementary. The effectiveness of experimental studies of complex systems is extremely low, since carrying out field experiments with a real system either requires large material costs and considerable time, or is practically impossible at all (for example, at the design stage when there is no real system). The effectiveness of theoretical research from the practical point of view fully manifests itself only when their results with the required degree of accuracy and reliability can be presented in the form of analytical relationships or modeling algorithms suitable for obtaining the relevant characteristics of the process of functioning of the systems under study.

System modeling tools.

The advent of modern computers was a decisive condition for the widespread introduction of analytical methods in the study of complex systems. It began to seem that models and methods, for example, of mathematical programming, would become a practical tool for solving control problems in large systems. Indeed, significant progress has been made in creating new mathematical methods for solving these problems, but mathematical programming has not become a practical tool for studying the process of functioning of complex systems, since mathematical programming models have proved to be too gross and imperfect for their effective use. The need to take into account the stochastic properties of the system, the non-determinism of the initial information, the presence of correlation links between a large number of variables and parameters characterizing processes in systems lead to the construction of complex mathematical models that can not be applied in engineering practice when studying such systems by an analytical method. Analytical relations suitable for practical calculations can be obtained only under simplifying assumptions, which usually essentially distort the actual picture of the process under study. Therefore, in recent times there has been a growing need to develop methods that would enable us to explore more adequate models at the design stage of systems. These circumstances lead to the fact that in the study of large systems, the methods of simulation are increasingly used [8, 11, 19, 25, 41, 54].

The most constructive means of solving engineering problems on the basis of modeling is now the computer. Modern computers can be divided into two groups: universal, primarily designed for performing computational work, and managers, allowing to carry out not only design work, but primarily designed to manage objects in real time. Control computers can be used both to control the technological process, experiment, and to implement various simulation models. Depending on whether it is possible to build a sufficiently accurate mathematical model of a real process, or because of the complexity of the object, it is impossible to penetrate into the depths of the functional connections of a real object and describe them with some analytical relations, two main ways of using computers can be considered: analytical models and as a means of simulation.

For a known analytical model, assuming that it accurately reflects the investigated side of the functioning of a real physical object, the computer is faced with the task of calculating the characteristics of the system by some mathematical relations when substituting numeric values. In this direction, computers have capabilities that are practically dependent on the order of the equation being solved and on the requirements for the speed of the solution, and both the computer and the AVM can be used.

When using a computer, an algorithm for calculating characteristics is developed, according to which programs are compiled (or generated with the help of an application package), which enable calculations on the required analytical relationships. The main task of the researcher is to try to describe the behavior of the real object of one of the known mathematical models.

The use of AVM, on the one hand, accelerates the process of solving the problem for fairly simple cases, on the other hand, errors can arise due to the drift of the parameters of individual blocks entering the AVM, limited by the accuracy with which the parameters introduced in machine, as well as hardware failures, etc.

The combination of computers and AVM is promising, that is, the use of hybrid computer facilities - hybrid computer complexes (GVK), which in a number of cases significantly speeds up the research process [12, 20, 37, 49].

In GVK it is possible to combine a high speed of functioning of analog means and high accuracy of calculations on the basis of digital means of computer technology. At the same time, it is possible to ensure the control of operations by the availability of digital devices. The experience of using computer technology in modeling problems shows that with the complexity of the object, the use of hybrid technology gives greater efficiency in terms of decision speed and the cost of performing operations.

A specific technical means of implementing the simulation model can be a computer, AVM and GVK. If the use of analogue technology accelerates the achievement of the final results, while preserving some visibility of the actual process, the use of digital means allows to monitor the implementation of the model, create programs for processing and storing the results of modeling, and ensure an effective dialogue between the researcher and the model.

Typically, the model is built according to the hierarchical principle, when the individual aspects of the object's functioning are analyzed in sequence and when the center of attention of the researcher is moved, the subsystems considered earlier pass to the external environment. The hierarchical structure of models can reveal the sequence in which the real object is studied, namely the sequence of transition from the structural (topological) level to the functional (algorithmic) level and from the functional to the parametric level.

The result of modeling largely depends on the adequacy of the original conceptual (descriptive) model, on the obtained degree of similarity in the description of the real object, the number of model implementations, and many other factors. In some cases, the complexity of the object does not allow not only to construct a mathematical model of the object, but also to give a sufficiently close cybernetic description, and it is promising to single out the part of the object that is most difficult to describe in mathematical terms and to include this real part of the physical object in the simulation model. Then the model is realized, on the one hand, on the basis of computer facilities, and on the other - there is a real part of the object. This greatly expands the possibilities and improves the reliability of simulation results.

The imitation system is realized on a computer and allows to investigate the simulation model N, given in the form of a certain set of individual block models and the relationships between them in their interaction in space and time in the implementation of a process. There are three main groups of blocks: blocks that characterize the simulated process of the system 5; blocks that display the external environment & pound; and its impact on the process being implemented; The blocks that play the auxiliary auxiliary role, providing the interaction of the first two, as well as performing additional functions for obtaining and processing the simulation results. In addition, the simulation system is characterized by a set of variables with which it is possible to control the process under study, and a set of initial conditions, when it is possible to change the conditions for carrying out the computer experiment.

Thus, the simulation system is a means of carrying out a computer experiment, and the experiment can be set up repeatedly, planned in advance, and the conditions for conducting it can be determined. It is necessary at the same time to choose a methodology for assessing the adequacy of the results obtained and to automate both the receiving processes and the processing of results during the machine experiment.

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