{"title":"模糊学习的遗传方法","authors":"M. Russo","doi":"10.1109/ISNFS.1996.603814","DOIUrl":null,"url":null,"abstract":"The approach proposed allows supervised approximation of multi-input/multi-output (MIMO) systems. Typically a small number of fuzzy rules are produced. The learning capacity is considerable, as is shown by the numerous applications developed. The paper gives a significant example of how the fuzzy models developed are generally better than those to be found in recent literature concerning both the approximation capability and simplicity.","PeriodicalId":187481,"journal":{"name":"1st International Symposium on Neuro-Fuzzy Systems, AT '96. Conference Report","volume":"244 ","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"A genetic approach to fuzzy learning\",\"authors\":\"M. Russo\",\"doi\":\"10.1109/ISNFS.1996.603814\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The approach proposed allows supervised approximation of multi-input/multi-output (MIMO) systems. Typically a small number of fuzzy rules are produced. The learning capacity is considerable, as is shown by the numerous applications developed. The paper gives a significant example of how the fuzzy models developed are generally better than those to be found in recent literature concerning both the approximation capability and simplicity.\",\"PeriodicalId\":187481,\"journal\":{\"name\":\"1st International Symposium on Neuro-Fuzzy Systems, AT '96. Conference Report\",\"volume\":\"244 \",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"1st International Symposium on Neuro-Fuzzy Systems, AT '96. Conference Report\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISNFS.1996.603814\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"1st International Symposium on Neuro-Fuzzy Systems, AT '96. Conference Report","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISNFS.1996.603814","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The approach proposed allows supervised approximation of multi-input/multi-output (MIMO) systems. Typically a small number of fuzzy rules are produced. The learning capacity is considerable, as is shown by the numerous applications developed. The paper gives a significant example of how the fuzzy models developed are generally better than those to be found in recent literature concerning both the approximation capability and simplicity.
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