{"title":"在混合连接主义、符号模型中学习模糊信息","authors":"S. G. Romaniuk, Lawrence O. Hall","doi":"10.1109/FUZZY.1992.258633","DOIUrl":null,"url":null,"abstract":"An implementation of fuzzy variables using pi-shaped membership functions is shown in a hybrid symbolic connectionist expert system tool that uses fuzzy logic to implement reasoning with uncertainty and imprecision and that can learn from imprecise data. A method of dynamically modifying the arms, or fuzzy part of the membership functions, during learning is shown. Examples illustrating the method are presented. The results indicate that the presented system is capable of learning membership functions for applications such as control or classification.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"72 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Learning fuzzy information in a hybrid connectionist, symbolic model\",\"authors\":\"S. G. Romaniuk, Lawrence O. Hall\",\"doi\":\"10.1109/FUZZY.1992.258633\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An implementation of fuzzy variables using pi-shaped membership functions is shown in a hybrid symbolic connectionist expert system tool that uses fuzzy logic to implement reasoning with uncertainty and imprecision and that can learn from imprecise data. A method of dynamically modifying the arms, or fuzzy part of the membership functions, during learning is shown. Examples illustrating the method are presented. The results indicate that the presented system is capable of learning membership functions for applications such as control or classification.<<ETX>>\",\"PeriodicalId\":222263,\"journal\":{\"name\":\"[1992 Proceedings] IEEE International Conference on Fuzzy Systems\",\"volume\":\"72 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-03-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[1992 Proceedings] IEEE International Conference on Fuzzy Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FUZZY.1992.258633\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FUZZY.1992.258633","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Learning fuzzy information in a hybrid connectionist, symbolic model
An implementation of fuzzy variables using pi-shaped membership functions is shown in a hybrid symbolic connectionist expert system tool that uses fuzzy logic to implement reasoning with uncertainty and imprecision and that can learn from imprecise data. A method of dynamically modifying the arms, or fuzzy part of the membership functions, during learning is shown. Examples illustrating the method are presented. The results indicate that the presented system is capable of learning membership functions for applications such as control or classification.<>
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