{"title":"规则插值中局部模糊性的维护","authors":"Tom Gedeon, L. Kóczy, Y. Huang, P. Wong","doi":"10.1109/INES.1997.632393","DOIUrl":null,"url":null,"abstract":"Approximate reasoning using fuzzy rule based systems has a wide application in, for example, industrial control, property prediction, and in pattern recognition areas. We introduce our method which is conservative with respect to the degree of local fuzziness in the rule base, and demonstrate its utility on a petroleum engineering problem.","PeriodicalId":161975,"journal":{"name":"Proceedings of IEEE International Conference on Intelligent Engineering Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Maintenance of local fuzziness in rule interpolation\",\"authors\":\"Tom Gedeon, L. Kóczy, Y. Huang, P. Wong\",\"doi\":\"10.1109/INES.1997.632393\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Approximate reasoning using fuzzy rule based systems has a wide application in, for example, industrial control, property prediction, and in pattern recognition areas. We introduce our method which is conservative with respect to the degree of local fuzziness in the rule base, and demonstrate its utility on a petroleum engineering problem.\",\"PeriodicalId\":161975,\"journal\":{\"name\":\"Proceedings of IEEE International Conference on Intelligent Engineering Systems\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-09-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of IEEE International Conference on Intelligent Engineering Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INES.1997.632393\",\"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 IEEE International Conference on Intelligent Engineering Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INES.1997.632393","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Maintenance of local fuzziness in rule interpolation
Approximate reasoning using fuzzy rule based systems has a wide application in, for example, industrial control, property prediction, and in pattern recognition areas. We introduce our method which is conservative with respect to the degree of local fuzziness in the rule base, and demonstrate its utility on a petroleum engineering problem.