{"title":"支持向量训练的递归模糊系统","authors":"I. Chung, Chia-Feng Juang, Cheng-Da Hsieh","doi":"10.1109/FUZZY.2010.5584494","DOIUrl":null,"url":null,"abstract":"This paper proposes a Support Vector-trained Recurrent Fuzzy System (SV-RFS) which comprises recurrent Takagi-Sugeno (TS) fuzzy if-then rules. The SV-RFS memories past input information by feeding the past firing strength of a fuzzy rule back to itself. The rules are generated based on a clustering-like algorithm. The feedback loop gains and consequent part parameters are learned through support vector regression (SVR) in order to improve system generalization ability. The SV-RFS is applied to noisy chaotic sequence prediction to verify its effectiveness.","PeriodicalId":377799,"journal":{"name":"International Conference on Fuzzy Systems","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Support Vector-trained Recurrent Fuzzy System\",\"authors\":\"I. Chung, Chia-Feng Juang, Cheng-Da Hsieh\",\"doi\":\"10.1109/FUZZY.2010.5584494\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a Support Vector-trained Recurrent Fuzzy System (SV-RFS) which comprises recurrent Takagi-Sugeno (TS) fuzzy if-then rules. The SV-RFS memories past input information by feeding the past firing strength of a fuzzy rule back to itself. The rules are generated based on a clustering-like algorithm. The feedback loop gains and consequent part parameters are learned through support vector regression (SVR) in order to improve system generalization ability. The SV-RFS is applied to noisy chaotic sequence prediction to verify its effectiveness.\",\"PeriodicalId\":377799,\"journal\":{\"name\":\"International Conference on Fuzzy Systems\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-07-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Fuzzy Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FUZZY.2010.5584494\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Fuzzy Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FUZZY.2010.5584494","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper proposes a Support Vector-trained Recurrent Fuzzy System (SV-RFS) which comprises recurrent Takagi-Sugeno (TS) fuzzy if-then rules. The SV-RFS memories past input information by feeding the past firing strength of a fuzzy rule back to itself. The rules are generated based on a clustering-like algorithm. The feedback loop gains and consequent part parameters are learned through support vector regression (SVR) in order to improve system generalization ability. The SV-RFS is applied to noisy chaotic sequence prediction to verify its effectiveness.
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