Track irregularity prediction method based on hybrid intelligent optimization LSTM

Abstract

The invention discloses a track irregularity prediction method based on a hybrid intelligent optimization LSTM, and the method comprises the steps: firstly carrying out the preprocessing of time series data, then optimizing the hyper-parameters of an LSTM model through a PSO algorithm, and determining a network structure of the LSTM model; and optimizing the initial weight threshold of the LSTM model by using a GA algorithm, and determining the weight threshold of the LSTM model. And finally training and predicting track irregularity data by using the determined hyper-parameter and weight threshold. According to the method for predicting the track irregularity data based on the LSTM-PSO-GA model, the problem that the precision is not high in the prediction process of a traditional prediction method is solved, the LSTM parameters are optimized through the PSO and GA algorithms, the problem that the model falls into a local optimal solution is avoided, and the prediction convergence speed is increased. Finally, the track irregularity data is predicted, and the track irregularity phenomenon is predicted more accurately.

Description

technical field [0001] The invention belongs to the technical field of time series forecasting, and in particular relates to a track irregularity forecasting method based on hybrid intelligent optimization LSTM. Background technique [0002] With the continuous development of high-speed and heavy-duty railway traffic in my country, the track repeatedly bears the load of rolling stock, gradually changing the geometric shape and spatial position of the track gauge, level, height, etc., resulting in track irregularities. Irregularity of the railway track directly threatens the safety of train operation. Based on the historical data of track irregularities, predicting the development trend of track irregularities and checking track safety can provide an important theoretical basis for track maintenance strategies. [0003] In recent years, many scholars at home and abroad have studied the problem of track irregularity prediction. Yong Antai predicted the state of the track qua...

AI Technical Summary

Problems solved by technology

Solve the problem that the traditional prediction method has low prediction accuracy for the orbital irregularity sequence data with large fluctuations

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