{"title":"混合模糊神经网络是一种通用逼近器","authors":"J. Buckley, U. Hayashi","doi":"10.1109/FUZZY.1994.343759","DOIUrl":null,"url":null,"abstract":"It is known that regular fuzzy neural nets, based on standard fuzzy arithmetic and the extension principle, can not be universal approximators. This negative result is surprising since (regular) neural nets are universal approximators. We argue that hybrid fuzzy neural nets, not necessarily based only on standard fuzzy arithmetic, can be universal approximators.<<ETX>>","PeriodicalId":153967,"journal":{"name":"Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Hybrid fuzzy neural nets are universal approximators\",\"authors\":\"J. Buckley, U. Hayashi\",\"doi\":\"10.1109/FUZZY.1994.343759\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"It is known that regular fuzzy neural nets, based on standard fuzzy arithmetic and the extension principle, can not be universal approximators. This negative result is surprising since (regular) neural nets are universal approximators. We argue that hybrid fuzzy neural nets, not necessarily based only on standard fuzzy arithmetic, can be universal approximators.<<ETX>>\",\"PeriodicalId\":153967,\"journal\":{\"name\":\"Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"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.1994.343759\",\"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.1994.343759","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Hybrid fuzzy neural nets are universal approximators
It is known that regular fuzzy neural nets, based on standard fuzzy arithmetic and the extension principle, can not be universal approximators. This negative result is surprising since (regular) neural nets are universal approximators. We argue that hybrid fuzzy neural nets, not necessarily based only on standard fuzzy arithmetic, can be universal approximators.<>