{"title":"Modelling of switched reluctance motor based on variable structure fuzzy-neural networks","authors":"Zheng Hongtao, Qiao Bin, Guo Zhijiang, Jian Jingping","doi":"10.1109/ICEMS.2001.971908","DOIUrl":null,"url":null,"abstract":"Switched reluctance motors (SRM) are almost always operated within the saturation region for a very large operation region. This yields very strong nonlinearities, which makes it very difficult to derive a comprehensive mathematical model for the behavior of the machine. This paper presents the variable structure fuzzy-neural networks model of SRM. Based on the Takagi-Sugeno fuzzy-neural networks, a variable structure and step learning arithmetic was presented. Then the fuzzy-simulation results show that this method is more precise and less time-consuming for convergence than BP neural networks model.","PeriodicalId":143007,"journal":{"name":"ICEMS'2001. Proceedings of the Fifth International Conference on Electrical Machines and Systems (IEEE Cat. No.01EX501)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ICEMS'2001. Proceedings of the Fifth International Conference on Electrical Machines and Systems (IEEE Cat. No.01EX501)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEMS.2001.971908","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Switched reluctance motors (SRM) are almost always operated within the saturation region for a very large operation region. This yields very strong nonlinearities, which makes it very difficult to derive a comprehensive mathematical model for the behavior of the machine. This paper presents the variable structure fuzzy-neural networks model of SRM. Based on the Takagi-Sugeno fuzzy-neural networks, a variable structure and step learning arithmetic was presented. Then the fuzzy-simulation results show that this method is more precise and less time-consuming for convergence than BP neural networks model.
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