{"title":"基于支持向量机的T-S模糊系统辨识","authors":"Yanli Deng, Jun Wang, Xiaodan Yan","doi":"10.1109/ICCA.2010.5524265","DOIUrl":null,"url":null,"abstract":"There are some problems in fuzzy system for modeling and identification, such as complexity of model construction, curse of dimensionality, poverty of generalization and error of real-time. To deal with these problems, support vector mechanism (SVM) for fuzzy system modeling has been introduced in this paper. And then the parameters have been optimized by error back-propagation training algorithm (BP algorithm). Experimental results demonstrate the effectiveness of the method.","PeriodicalId":155562,"journal":{"name":"IEEE ICCA 2010","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"T-S fuzzy system identification based on support vector machine\",\"authors\":\"Yanli Deng, Jun Wang, Xiaodan Yan\",\"doi\":\"10.1109/ICCA.2010.5524265\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"There are some problems in fuzzy system for modeling and identification, such as complexity of model construction, curse of dimensionality, poverty of generalization and error of real-time. To deal with these problems, support vector mechanism (SVM) for fuzzy system modeling has been introduced in this paper. And then the parameters have been optimized by error back-propagation training algorithm (BP algorithm). Experimental results demonstrate the effectiveness of the method.\",\"PeriodicalId\":155562,\"journal\":{\"name\":\"IEEE ICCA 2010\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-06-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE ICCA 2010\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCA.2010.5524265\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE ICCA 2010","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCA.2010.5524265","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
T-S fuzzy system identification based on support vector machine
There are some problems in fuzzy system for modeling and identification, such as complexity of model construction, curse of dimensionality, poverty of generalization and error of real-time. To deal with these problems, support vector mechanism (SVM) for fuzzy system modeling has been introduced in this paper. And then the parameters have been optimized by error back-propagation training algorithm (BP algorithm). Experimental results demonstrate the effectiveness of the method.