Wavelet neural network prediction of electric vehicle chaotic vibration signals

被引：0

作者：

Niu Z. ^{[1
]}

Wu G. ^{[1
,2
]}

机构：

[1] College of Automotive Studies, Tongji University, Shanghai

[2] Institute of Industrial Science, The University of Tokyo, Tokyo

来源：

Zhendong yu Chongji/Journal of Vibration and Shock | 2018年 / 37卷 / 08期

关键词：

Chaotic time series; Electric vehicle; Wavelet neural network;

D O I：

10.13465/j.cnki.jvs.2018.08.019

中图分类号：

学科分类号：

摘要：

A vehicle experiment was carried out to study the chaotic dynamics of electric vehicles, and chaotic time series were predicted by using wavelet neural network. First, the experiment of electric vehicle on medium-Belgian road was carried out, the vertical vibration acceleration signal of the right front wheel center and battery bottom center were obtained. Second, time-frequency analysis, three-dimensional phase diagrams and the Poincaré sections of the signals were obtained. The time delay was calculated by using the mutual information method, and also minimum embedding dimension was got with the Cao method, the largest Lyapunov exponent was got with Wolf method. The presence of chaotic motions in the vertical acceleration signal was found. Finally, chaotic time series of the right front wheel center vertical signal was predicted using wavelet neural network. It is found that the use of wavelet neural network to predict chaotic time series can achieve better results. © 2018, Editorial Office of Journal of Vibration and Shock. All right reserved.

引用

页码：120 / 124

页数：4

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