The article formulates the relevance of the development of methods for increasing the efficiency of using mainline freight electric locomotives by reducing electricity losses by improving on-board driving systems and decision support through the use of intelligent systems for generating optimal operating modes. As a parameter under study, when solving the problem of predicting the recommended operating mode of an electric locomotive, the setting of the traction force was adopted, since it has a significant effect on the amount of electrical energy consumption, determines the nature of the locomotive movement and is not tied to the design features of a particular series of electric locomotives. To solve this problem, long short-term memory (LSTM) networks were chosen. This network architecture is insensitive to time gaps between events, which allows them to be used in tasks where long-term and delayed effects must be taken into account. This property is achieved by isolating two feedbacks: the hidden state and the cell state, and adding gates to control them. As a result, a tuned artificial recurrent neural network was obtained, which allows calculating the traction force settings for the next 10 s based on the data on the locomotive operation over the last 30 s.


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    Titel :

    Control Method for Mainline Freight Electric Locomotives with the Possibility of Adjusting Movement Modes


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Guda, Alexander (Herausgeber:in) / Istomin, Stanislav (Autor:in) / Perestenko, Artem (Autor:in) / Shantarenko, Sergey (Autor:in)

    Kongress:

    International School on Neural Networks, Initiated by IIASS and EMFCSC ; 2022 ; St.Petersburg, Russia February 08, 2022 - February 10, 2022



    Erscheinungsdatum :

    16.11.2022


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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