机械制造与自动化Issue(2)：130-132,3.DOI:10.19344/j.cnki.issn1671-5276.2018.02.035
基于粒子群算法优化BP神经网络的SRM磁链模型
SRM Flux Linkage Model of Optimizing BP Neural Network Based on PSO Algorithm
郝娟1
作者信息
- 1. 河海大学 能源与电气学院,江苏 南京211100
- 折叠
摘要
Abstract
This paper takes the current and flux linkage of 6/4 SRM motor as the input and its rotor position angle as the output to fit the flux-current-angle model and makes an experiment on its simulation in MATLAB/Simulink.To improve the training efficiency and simplify the fitting model, according to PSO algorithm, the number of neurons of double hidden layers of BP neural network is opti-mized and the results show that it has higher training result.关键词
开关磁阻电机/BP神经网络/粒子群算法Key words
switched reluctance motor/BP neural network/PSO algorithm分类
信息技术与安全科学引用本文复制引用
郝娟..基于粒子群算法优化BP神经网络的SRM磁链模型[J].机械制造与自动化,2018,(2):130-132,3.
