{"title":"语言隶属函数的调优方法","authors":"B. Chung, Jun-Ho Oh","doi":"10.1109/FUZZY.1994.343720","DOIUrl":null,"url":null,"abstract":"A learning method which can tune the linguistic membership functions is presented. To accomplish such a method, the fuzzy inference mechanism must be expressed by the membership functions without losing a physical sense. The fuzzy subsets are then described by only a few membership functions. When both the control input and the linguistic membership function are tuned in the fuzzy controller, the fuzzy rules after the training can be expressed by the linguistic membership functions maintaining their physical meaning.<<ETX>>","PeriodicalId":153967,"journal":{"name":"Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference","volume":"2 12","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Tuning method of linguistic membership functions\",\"authors\":\"B. Chung, Jun-Ho Oh\",\"doi\":\"10.1109/FUZZY.1994.343720\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A learning method which can tune the linguistic membership functions is presented. To accomplish such a method, the fuzzy inference mechanism must be expressed by the membership functions without losing a physical sense. The fuzzy subsets are then described by only a few membership functions. When both the control input and the linguistic membership function are tuned in the fuzzy controller, the fuzzy rules after the training can be expressed by the linguistic membership functions maintaining their physical meaning.<<ETX>>\",\"PeriodicalId\":153967,\"journal\":{\"name\":\"Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference\",\"volume\":\"2 12\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FUZZY.1994.343720\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FUZZY.1994.343720","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A learning method which can tune the linguistic membership functions is presented. To accomplish such a method, the fuzzy inference mechanism must be expressed by the membership functions without losing a physical sense. The fuzzy subsets are then described by only a few membership functions. When both the control input and the linguistic membership function are tuned in the fuzzy controller, the fuzzy rules after the training can be expressed by the linguistic membership functions maintaining their physical meaning.<>
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