{"title":"基于模糊聚类的去模糊化","authors":"H. Genther, T. Runkler, M. Glesner","doi":"10.1109/FUZZY.1994.343943","DOIUrl":null,"url":null,"abstract":"We develop a modified fuzzy clustering algorithm for parametric defuzzification in fuzzy rule base systems. Using examples and basic defuzzification properties we compare defuzzification by clustering with the standard defuzzification methods COG (Center of Gravity) and MOM (Mean of Maxima). Concerning fuzzy sets with forbidden zones the new method proves to be superior. We present how heuristic preprocessing and quality measures are used for appropriate parameter selection.<<ETX>>","PeriodicalId":153967,"journal":{"name":"Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":"{\"title\":\"Defuzzification based on fuzzy clustering\",\"authors\":\"H. Genther, T. Runkler, M. Glesner\",\"doi\":\"10.1109/FUZZY.1994.343943\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We develop a modified fuzzy clustering algorithm for parametric defuzzification in fuzzy rule base systems. Using examples and basic defuzzification properties we compare defuzzification by clustering with the standard defuzzification methods COG (Center of Gravity) and MOM (Mean of Maxima). Concerning fuzzy sets with forbidden zones the new method proves to be superior. We present how heuristic preprocessing and quality measures are used for appropriate parameter selection.<<ETX>>\",\"PeriodicalId\":153967,\"journal\":{\"name\":\"Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"24\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FUZZY.1994.343943\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FUZZY.1994.343943","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We develop a modified fuzzy clustering algorithm for parametric defuzzification in fuzzy rule base systems. Using examples and basic defuzzification properties we compare defuzzification by clustering with the standard defuzzification methods COG (Center of Gravity) and MOM (Mean of Maxima). Concerning fuzzy sets with forbidden zones the new method proves to be superior. We present how heuristic preprocessing and quality measures are used for appropriate parameter selection.<>
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