The Identification for Switched Reluctance Motor Drive System Used in Electric Vehicles Based on Mixed Genetic Neural Networks

被引：0
|
作者：
Zhao, Hong [1 ]
机构：
[1] China Jiliang Univ, Sch Mechatron Engn, Hangzhou, Zhejiang, Peoples R China
来源：
2011 INTERNATIONAL CONFERENCE ON MACHINE INTELLIGENCE (ICMI 2011), PT 2 | 2011年 / 4卷
关键词：
Electric vehicles; switched reluctance motor; identification; mixed genetic algorithms; neural network; PROPULSION;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Based on the characteristics of the nonlinear and the large range of structures and parameters of switched reluctance motor drive system used in electric vehicles, a new method that the combination of mixed genetic algorithms and neural network is put forward to achieve the identification of switched reluctance motor drive system. The principle is expounded and corresponding algorithm and formulas are presented as well. The method combines the advantages of the optimum genetic algorithms and neural networks which overcomes the shortcomings of traditional BP networks such as the slow learning rate and liable to converge to the local minima, The simulation results demonstrate that this method is quite practicable for its fast and exact closing to its real system.
引用
收藏
页码：29 / 34
页数：6
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