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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    Title :

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


    Contributors:
    Tong, Xinyu (author) / Meng, Haining (author) / Feng, Kai (author) / Ji, Wenjiang (author) / Zheng, Yi (author) / Hei, Xinhong (author)


    Publication date :

    2021-10-01


    Size :

    662904 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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