{"title":"容错开关磁阻电机特性的神经遗传学模型","authors":"L. Belfore, A. Arkadan","doi":"10.1109/IEMDC.1997.604212","DOIUrl":null,"url":null,"abstract":"This paper examines the feasibility of using artificial neural networks (ANNs) and genetic algorithms (GAs) to develop discrete time dynamic models for fault free and faulted switched reluctance motor (SRM) drive systems. The results of using the ANN GA based (neurogenetic) model to predict the performance characteristics of prototype SRM drive motor under normal and abnormal operating conditions, are presented and verified by comparison to test data.","PeriodicalId":176640,"journal":{"name":"1997 IEEE International Electric Machines and Drives Conference Record","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Neurogenetic models for the characterization of fault tolerant switched reluctance motors\",\"authors\":\"L. Belfore, A. Arkadan\",\"doi\":\"10.1109/IEMDC.1997.604212\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper examines the feasibility of using artificial neural networks (ANNs) and genetic algorithms (GAs) to develop discrete time dynamic models for fault free and faulted switched reluctance motor (SRM) drive systems. The results of using the ANN GA based (neurogenetic) model to predict the performance characteristics of prototype SRM drive motor under normal and abnormal operating conditions, are presented and verified by comparison to test data.\",\"PeriodicalId\":176640,\"journal\":{\"name\":\"1997 IEEE International Electric Machines and Drives Conference Record\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-05-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"1997 IEEE International Electric Machines and Drives Conference Record\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IEMDC.1997.604212\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"1997 IEEE International Electric Machines and Drives Conference Record","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEMDC.1997.604212","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Neurogenetic models for the characterization of fault tolerant switched reluctance motors
This paper examines the feasibility of using artificial neural networks (ANNs) and genetic algorithms (GAs) to develop discrete time dynamic models for fault free and faulted switched reluctance motor (SRM) drive systems. The results of using the ANN GA based (neurogenetic) model to predict the performance characteristics of prototype SRM drive motor under normal and abnormal operating conditions, are presented and verified by comparison to test data.
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