{"title":"模糊专家系统中的规则生成算法","authors":"K. Dmitry, V. Dmitry","doi":"10.1109/ICPR.2004.149","DOIUrl":null,"url":null,"abstract":"Although using fuzzy logic in control systems has become widely established as an appropriate approach, its application in area of pattern recognition and data mining seems to be restricted. These systems have several bottlenecks mainly concerning fuzzy rules generation and fuzzy sets forming. The state-of-the-art technique here is neuro-fuzzy approach which has some disadvantages. In this article an algorithm is considered for rules generation based on alternative principles.","PeriodicalId":335842,"journal":{"name":"Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.","volume":"51 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"An algorithm for rule generation in fuzzy expert systems\",\"authors\":\"K. Dmitry, V. Dmitry\",\"doi\":\"10.1109/ICPR.2004.149\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Although using fuzzy logic in control systems has become widely established as an appropriate approach, its application in area of pattern recognition and data mining seems to be restricted. These systems have several bottlenecks mainly concerning fuzzy rules generation and fuzzy sets forming. The state-of-the-art technique here is neuro-fuzzy approach which has some disadvantages. In this article an algorithm is considered for rules generation based on alternative principles.\",\"PeriodicalId\":335842,\"journal\":{\"name\":\"Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.\",\"volume\":\"51 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-08-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPR.2004.149\",\"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 the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPR.2004.149","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An algorithm for rule generation in fuzzy expert systems
Although using fuzzy logic in control systems has become widely established as an appropriate approach, its application in area of pattern recognition and data mining seems to be restricted. These systems have several bottlenecks mainly concerning fuzzy rules generation and fuzzy sets forming. The state-of-the-art technique here is neuro-fuzzy approach which has some disadvantages. In this article an algorithm is considered for rules generation based on alternative principles.