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Nowadays fuzzy neural networks are widely used for modeling of complex industrial processes. The paper considers the application of fuzzy logic to generate a mathematical model of electricity consumption in rail transport for example, traction substation Dorogino. Algorithm for choice of the structure of fuzzy neural network including the species and number of membership functions for input and the number of training cycles is presented. Comparative analysis of the structures by evaluating the mean square error is made.

The article analyzes the results of the assessment operating time and power consumption of compressor equipment service locomotive depo Moscow West-Siberian Railway. The algorithms for data preparation for modeling and evaluating the efficiency of the production process are presented. The main factors influencing the power consumption of the compressor equipment are determined and the results of the assessment of their influence are presented. On the basis of the dependences obtained, a mathematical model of the process of power consumption of compressor equipment using fuzzy neural networks and its accuracy is determined. The interface of the developed program complex is presented at an estimation of influence of factors on process of power consumption of the equipment service locomotive depo Moscow West-Siberian Railway.