高速磁浮列车悬浮间隙仿真预测

Suspension Gap Prediction of High-speed Maglev Train

摘要:

基于长短时记忆（LSTM）神经网络提出了一种可用于高速磁浮列车的电磁铁悬浮间隙预测方法。考虑高速磁浮列车运行过程中受到的气动荷载，建立了列车仿真模型并计算列车的动态响应；通过PyCharm建立LSTM神经网络，并以高速磁浮列车仿真模型计算结果为样本集，构建了高速磁浮列车电磁铁悬浮间隙预测模型。最后，通过对预测模型计算结果和评价指标进行评判，验证了所提出的电磁铁间隙预测算法的准确性。

Abstract:

An electromagnet suspension gap prediction method was proposed based on long short-term memory （LSTM） neural network for high-speed maglev trains. Firstly， considering the aerodynamic load during the operation of high-speed maglev train， a train simulation model was established and the dynamic response of the train was calculated. Secondly， the LSTM neural network was established by PyCharm and the prediction model of the electromagnet suspension gap for high-speed maglev trains was built by taking the simulation calculation results as the sample set. Finally， the accuracy of the proposed electromagnet suspension gap prediction algorithm was verified by judging the calculation results and evaluation indexes of the prediction model.