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.
A STL-GALSTM Model to Predict the Track Irregularity of High-Speed Railway
01.10.2021
662904 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Multi-Stage Linear Prediction Model for Railway Track Irregularity
Tema Archiv | 2013
|Track Irregularity Inspection Method by Commercial Railway Vehicles
British Library Online Contents | 1997
|Track Irregularity Inspection Method by Commercial Railway Vehicles
Online Contents | 1997
|Track Irregularity Inspection Method by Commercial Railway Vehicle
IuD Bahn | 1997
|Railway Track Irregularity Measuring by GNSS/INS Integration
Online Contents | 2015
|