The accurate prediction of track irregularity has great practical significance for high-speed railway maintenance, train safety, and comfortable operation. In this paper, we propose a STL-GALSTM model to predict the track irregularity for high-speed railway. First, the seasonal-trend decomposition using loess (STL) method is utilized to decompose the track irregularity time series into the trend, seasonal, and remainder components. Then the Long short-term memory (LSTM) model is used to predict the decomposed components. And then the genetic algorithm (GA) optimizes the structure of LSTM. The experiment results show the STL-GALSTM can obtain an accurate track irregularity prediction value.


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    Titel :

    A STL-GALSTM Model to Predict the Track Irregularity of High-Speed Railway


    Beteiligte:
    Tong, Xinyu (Autor:in) / Meng, Haining (Autor:in) / Feng, Kai (Autor:in) / Ji, Wenjiang (Autor:in) / Zheng, Yi (Autor:in) / Hei, Xinhong (Autor:in)


    Erscheinungsdatum :

    01.10.2021


    Format / Umfang :

    662904 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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