In recent years, major achievements in various fields of science and technology are inextricably linked with the process of improving computers. The sphere of computer operation is rapidly developing

branch of human practice, stimulating the development of new theoretical and applied directions [35]. The resources of modern information and computing technology make it possible to set and solve mathematical problems of such complexity that in the recent past seemed unrealizable, for example, the modeling of large systems.

Analytical and simulation methods

Historically, the analytical approach to the study of systems, when the computer was used as a calculator for analytical dependencies. Analysis of the characteristics of the processes of functioning of large systems using only analytical methods of investigation usually encounters significant difficulties, leading to the need for a significant simplification of models either at the stage of their construction or in the process of working with the model, which can lead to unreliable results.

Therefore, at the present time, along with the construction of analytical models, much attention is paid to the problems of estimating the characteristics of large systems based on simulation models , implemented on modern computers with high speed and large amount of RAM. Moreover, the perspective of simulation as a method for studying the characteristics of the process of functioning of large systems increases with increasing speed and RAM of the computer, with the development of mathematical support, the improvement of data banks and peripheral devices for the organization of interactive modeling systems. This, in turn, contributes to the emergence of new "pure machine" methods for solving the problems of studying large systems on the basis of organizing simulation experiments with their models. Moreover, the orientation toward automated workstations on the basis of personal computers for the implementation of experiments with simulation models of large systems makes it possible to carry out not only the analysis of their characteristics, but also to solve the problems of structural, algorithmic and parametric synthesis of such systems for given efficiency criteria and constraints [4, 9, 18, 23].

The successes achieved in using computer aids for modeling purposes often create the illusion that the use of modern computers guarantees the possibility of investigating a system of any complexity. It ignores the fact that the basis of any model is labor-intensive in terms of time and material resources, preliminary study of the phenomena taking place in the original object. And on how thoroughly the real phenomena are studied, how well they are formalized and algorithmized, ultimately the success of the simulation of a particular object depends.

Tools for modeling systems. The expansion of the modeling capabilities of various classes of large systems is inextricably linked with the improvement of computer facilities and communication technology. A promising direction is the creation for the purposes of modeling of hierarchical multi-computer systems and networks [2, 7, 12, 25, 41].

When creating large systems, their components are developed by different teams that use modeling tools to analyze and synthesize individual subsystems. In this case, developers need quick access to software and hardware modeling tools, as well as an operational exchange of simulation results for individual interacting subsystems. Thus, there is a need to create interactive modeling systems, which are characterized by the following features: the possibility of simultaneous operation of many users involved in the development of one or more systems, user access to the software and technical resources of the modeling system, including databases and knowledge, application packages modeling, providing an interactive mode of operation with various computers and devices, including digital and analog computers, the mouth ovki natural and physical simulation, elements of real systems, and so on. n., dispatching works in the system modeling , and the provision of various services to users, including training to work with interactive simulation system while providing user-friendly interface.

Depending on the specifics of the objects under study, in some cases, modeling on analog computers (AVM) is effective. At the same time, it should be borne in mind that AVMs are much inferior to computers in terms of accuracy and logical capabilities, but in terms of speed, circuit simplicity of implementation, compatibility with external information sensors, AVMs surpass computers or at least not inferior to them.

Modeling based on hybrid (analog-digital) computer systems is promising for complex dynamic objects. Such complexes realize the advantages of digital and analogue modeling and allow the most efficient use of computer resources and AVM in a single complex. When using hybrid modeling systems, questions of interaction with sensors installed on real objects are simplified, which in turn allows to carry out combined modeling using the analog-digital part of the model and the natural part of the object [11, 19]. Such hybrid modeling complexes can be part of a multi-machine computational complex, which further extends its capabilities from the point of view of simulated classes of large systems.

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