{"title":"基于模糊教学输入的神经专家系统","authors":"Y. Hayashi","doi":"10.1109/FUZZY.1992.258661","DOIUrl":null,"url":null,"abstract":"The author previously (1990, 1991) proposed a fuzzy neural expert system and provided a method to extract automatically fuzzy IF-THEN rules from a trained neural network. The previous work is extended and a neural expert system is proposed using fuzzy teaching input. The neural expert system can perform generalization of the information derived from training data with fuzzy teaching input and embodiment of knowledge in the form of a fuzzy neural network, where the fuzzy teaching input is subjectively given by domain experts: and extraction of fuzzy IF-THEN rules with linguistic relative importance of each proposition in an antecedent (IF-part) from a trained neural network. A method is proposed to extract automatically fuzzy IF-THEN rules from the trained neural network generated by training data with fuzzy teaching input.<<ETX>>","PeriodicalId":222263,"journal":{"name":"[1992 Proceedings] IEEE International Conference on Fuzzy Systems","volume":"661 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"26","resultStr":"{\"title\":\"A neural expert system using fuzzy teaching input\",\"authors\":\"Y. Hayashi\",\"doi\":\"10.1109/FUZZY.1992.258661\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The author previously (1990, 1991) proposed a fuzzy neural expert system and provided a method to extract automatically fuzzy IF-THEN rules from a trained neural network. The previous work is extended and a neural expert system is proposed using fuzzy teaching input. The neural expert system can perform generalization of the information derived from training data with fuzzy teaching input and embodiment of knowledge in the form of a fuzzy neural network, where the fuzzy teaching input is subjectively given by domain experts: and extraction of fuzzy IF-THEN rules with linguistic relative importance of each proposition in an antecedent (IF-part) from a trained neural network. A method is proposed to extract automatically fuzzy IF-THEN rules from the trained neural network generated by training data with fuzzy teaching input.<<ETX>>\",\"PeriodicalId\":222263,\"journal\":{\"name\":\"[1992 Proceedings] IEEE International Conference on Fuzzy Systems\",\"volume\":\"661 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-03-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"26\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[1992 Proceedings] IEEE International Conference on Fuzzy Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FUZZY.1992.258661\",\"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.1992.258661","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The author previously (1990, 1991) proposed a fuzzy neural expert system and provided a method to extract automatically fuzzy IF-THEN rules from a trained neural network. The previous work is extended and a neural expert system is proposed using fuzzy teaching input. The neural expert system can perform generalization of the information derived from training data with fuzzy teaching input and embodiment of knowledge in the form of a fuzzy neural network, where the fuzzy teaching input is subjectively given by domain experts: and extraction of fuzzy IF-THEN rules with linguistic relative importance of each proposition in an antecedent (IF-part) from a trained neural network. A method is proposed to extract automatically fuzzy IF-THEN rules from the trained neural network generated by training data with fuzzy teaching input.<>