Russian Federation
Russian Federation
Russian Federation
Russian Federation
Russian Federation
Purpose: To select and verify methods and algorithms of machine learning to build dynamic models of a train energy-efficient movement in real time. Earlier, the use of DC electric locomotives for driving freight trains was assessed and the factors influencing the energy-efficient train movement were identified. This paper is devoted to the latest innovations in the field of automated train control within the framework of the JSC “Russian Railways” grant project for young scientists to carry out scientific research aimed at creating new equipment and technologies for railway transport. Methods: Optimization methods for machine learning were applied using model nonlinear dynamic systems. Results: The Levenberg-Marquardt method has been found most appropriate for determining the optimal position of the train driver controller by using recurrent neural network training. Graphical dependences of error histograms and total mean square error (MSE) variations in the process of artificial neural network training have been obtained. Practical significance: The results of the research can be used in the development of hardware and software systems using artificial intelligence methods and algorithms aimed at energy-efficiency improvement in transportation process.
Machine learning, locomotive, artificial intelligence, automated control, energy-optimal schedule, energy efficiency
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