NEURAL NETWORK USAGE FOR PROCESS OPTIMIZATION OF ROLLING STOCK SUBWAY
Abstract
The analysis of the control systems operating modes of traction motors DC subway cars and trains processes Kharkov Metro. Given the highly dynamic acceleration and braking cars, and relatively short distances between stations, seems the most real optimization of trains using neural networks adaptive resonance theory. The architecture of associative memory, which includes two modules storing code spans and the laws of motion of trains in the form of graphs. On the basis of neural networks adaptive resonance theory has developed an original associative memory structure for the storage of trains underground control laws to minimize energy costs and provides a timetable and comfortable transportation of passengers.Keywords: neural network, adaptive resonance theory, associative memory, the optimal traffic control.
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