{"title":"神经模糊系统的混合进化设计","authors":"R. El hamdi, M. Njah, M. Chtourou","doi":"10.1109/SSD.2010.5585517","DOIUrl":null,"url":null,"abstract":"In this paper, a hybrid evolutionary approach, combining the theory of learning automata (LA) and the steady-state genetic algorithm (SSGA), is proposed for design of TSK-type fuzzy model (TFM). In the proposed memetic approach, both the number of fuzzy rules and adjustable parameters in the TFM are designed concurrently.","PeriodicalId":432382,"journal":{"name":"2010 7th International Multi- Conference on Systems, Signals and Devices","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A hybrid evolutionary design of neuro-fuzzy systems\",\"authors\":\"R. El hamdi, M. Njah, M. Chtourou\",\"doi\":\"10.1109/SSD.2010.5585517\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a hybrid evolutionary approach, combining the theory of learning automata (LA) and the steady-state genetic algorithm (SSGA), is proposed for design of TSK-type fuzzy model (TFM). In the proposed memetic approach, both the number of fuzzy rules and adjustable parameters in the TFM are designed concurrently.\",\"PeriodicalId\":432382,\"journal\":{\"name\":\"2010 7th International Multi- Conference on Systems, Signals and Devices\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-06-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 7th International Multi- Conference on Systems, Signals and Devices\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SSD.2010.5585517\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 7th International Multi- Conference on Systems, Signals and Devices","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSD.2010.5585517","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A hybrid evolutionary design of neuro-fuzzy systems
In this paper, a hybrid evolutionary approach, combining the theory of learning automata (LA) and the steady-state genetic algorithm (SSGA), is proposed for design of TSK-type fuzzy model (TFM). In the proposed memetic approach, both the number of fuzzy rules and adjustable parameters in the TFM are designed concurrently.
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