ARTIFICIAL NEURAL NETWORKS USED IN THE PROBLEMS OF OPTIMIZING THE TECHNICAL OPERATION OF THE ELECTRIC TRANSPORT MOVABLE COMPOSITION
Abstract
Optimization problems often require the use of optimization techniques, which minimize or maximize certain target functions. Sometimes the problems that need to be optimized are not linear or polynomial; they can not be precisely resolved, and they must be approximated. In these cases, it is necessary to apply a heuristic that is capable of solving such problems. Some algorithms linearize the constraints and objective functions at a certain point of space, using derivatives and partial derivatives for some cases; in other cases, evolution algorithms are used to approximate the solution. In this paper, we propose the use of artificial neural networks to approximate the objective function in optimization problems, which allows us to apply other methods of solving the problem. The target function is approximated by nonlinear regression, which can be used to solve the optimization problem. The derivative of the new target function must be polynomial so that one can calculate the solution to the optimization problem.
Recently, many methods and algorithms of automated control of the technical state of rolling stock of electric vehicles, both foreign and domestic scientists, are being developed. This is due to increasing requirements for transport companies operating in difficult circumstances of uncertainty about future factors affecting the company's performance. Fuzzy predictability of future actions determines the generation and decision making using the fuzzy logic tool.In the article the authors analyze the existing approaches to optimization of intelligent transport management systems. The urgency of the topic is due to a number of issues that exist when making the optimal solution for transport companies, while complying with the conditions of minimum use of the main resources, and ensuring the safe operation of the enterprise as a whole.The positive scientific result in this article is that the concept of constructing hierarchical intelligent control systems by complex dynamic-changing objects of transport enterprises, operating in conditions of significant uncertainty, is proposed. Among them, it is possible to define multilevel intelligent control and control systems as part of algorithmic support. In this case, it is necessary to apply management algorithms based on the use of knowledge, teaching methods, soft computing, neural networks based on fuzzy logic.
Keywords: neural networks, optimization problems, nonlinear optimization, rolling stock, electric transport.
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