Equivalent Motor Radiation of an Electric Vehicle Based on Neural Network Approach

被引：0

作者：

Yu, Yaxin ^{[1
]}

Du, Xinwei ^{[1
]}

Xu, Bo ^{[1
]}

Wang, Chuhan ^{[1
]}

Xiao, Lingyu ^{[1
]}

Zhang, Zan ^{[1
]}

机构：

[1] Changan Univ, Coll Elect & Control Engn, Xian, Peoples R China

来源：

2019 INTERNATIONAL SYMPOSIUM ON ANTENNAS AND PROPAGATION (ISAP 2019) | 2019年

关键词：

feedforward neural network; electric vehicle; dipole antenna; electromagnetic radiation; OPTIMIZATION;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The feedforward neural network method is utilized in this paper for predicting the desired electromagnetic radiation properties of the motor of an electric vehicle. The neural network is first fully trained by simulation data and its predicted field values are then compared with those obtained through "traditional" FEKO simulations. The comparison has demonstrated a similar level of accuracy for both approaches, but the simulation time and cost can be dramatically reduced by employing the neural network method.

引用

页数：3

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