{"title":"一种用于模糊系统结构学习的简化结构演化方法","authors":"Di Wang, Xiao-Jun Zeng, J. Keane","doi":"10.1109/EAIS.2011.5945914","DOIUrl":null,"url":null,"abstract":"This paper proposes a Simplified Structure Evolving Method (SSEM) for Fuzzy Systems, which improves our previous work of Structure Evolving Learning Method for Fuzzy Systems (SELM [1]). SSEM keeps all the advantages of SELM [1] and improve SELM by starting with the simplest fuzzy rule set with only one fuzzy rule (instead of 2n fuzzy rules in SELM) as the starting point. By doing this SSEM is able to select the most efficient partitions and the most efficient attributes as well for system identification. This improvement enables fuzzy systems applicable to high dimensional problems. Benchmark examples with high dimension inputs are given to illustrate the advantages of the proposed algorithm.","PeriodicalId":243348,"journal":{"name":"2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"240 5","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Simplified Structure Evolving Method for Fuzzy System structure learning\",\"authors\":\"Di Wang, Xiao-Jun Zeng, J. Keane\",\"doi\":\"10.1109/EAIS.2011.5945914\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a Simplified Structure Evolving Method (SSEM) for Fuzzy Systems, which improves our previous work of Structure Evolving Learning Method for Fuzzy Systems (SELM [1]). SSEM keeps all the advantages of SELM [1] and improve SELM by starting with the simplest fuzzy rule set with only one fuzzy rule (instead of 2n fuzzy rules in SELM) as the starting point. By doing this SSEM is able to select the most efficient partitions and the most efficient attributes as well for system identification. This improvement enables fuzzy systems applicable to high dimensional problems. Benchmark examples with high dimension inputs are given to illustrate the advantages of the proposed algorithm.\",\"PeriodicalId\":243348,\"journal\":{\"name\":\"2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)\",\"volume\":\"240 5\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-04-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EAIS.2011.5945914\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EAIS.2011.5945914","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Simplified Structure Evolving Method for Fuzzy System structure learning
This paper proposes a Simplified Structure Evolving Method (SSEM) for Fuzzy Systems, which improves our previous work of Structure Evolving Learning Method for Fuzzy Systems (SELM [1]). SSEM keeps all the advantages of SELM [1] and improve SELM by starting with the simplest fuzzy rule set with only one fuzzy rule (instead of 2n fuzzy rules in SELM) as the starting point. By doing this SSEM is able to select the most efficient partitions and the most efficient attributes as well for system identification. This improvement enables fuzzy systems applicable to high dimensional problems. Benchmark examples with high dimension inputs are given to illustrate the advantages of the proposed algorithm.
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