{"title":"模糊神经计算技术及其在建模和控制中的应用","authors":"M. Gupta, M. Gorzałczany","doi":"10.1109/IJCNN.1991.170604","DOIUrl":null,"url":null,"abstract":"The authors present a model building technique which combines the strength of the fuzzy set theory and the neural network based structures. This technique can simultaneously deal with two types of knowledge, a nonfuzzy one and a fuzzy one, which usually describe the behavior of complex processes. The proposed method can also be directly applied to the construction of a new type of intelligent fuzzy controller. Some aspects of the adequacy of this fuzzy neuro-computational model are also discussed. A numerical example is provided.<<ETX>>","PeriodicalId":211135,"journal":{"name":"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1991-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"Fuzzy neuro-computational technique and its application to modelling and control\",\"authors\":\"M. Gupta, M. Gorzałczany\",\"doi\":\"10.1109/IJCNN.1991.170604\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The authors present a model building technique which combines the strength of the fuzzy set theory and the neural network based structures. This technique can simultaneously deal with two types of knowledge, a nonfuzzy one and a fuzzy one, which usually describe the behavior of complex processes. The proposed method can also be directly applied to the construction of a new type of intelligent fuzzy controller. Some aspects of the adequacy of this fuzzy neuro-computational model are also discussed. A numerical example is provided.<<ETX>>\",\"PeriodicalId\":211135,\"journal\":{\"name\":\"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1991-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IJCNN.1991.170604\",\"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] 1991 IEEE International Joint Conference on Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.1991.170604","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fuzzy neuro-computational technique and its application to modelling and control
The authors present a model building technique which combines the strength of the fuzzy set theory and the neural network based structures. This technique can simultaneously deal with two types of knowledge, a nonfuzzy one and a fuzzy one, which usually describe the behavior of complex processes. The proposed method can also be directly applied to the construction of a new type of intelligent fuzzy controller. Some aspects of the adequacy of this fuzzy neuro-computational model are also discussed. A numerical example is provided.<>
![[Proceedings] 1991 IEEE International Joint Conference on Neural Networks](/Content/css/sci/img/zetp.jpg)
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
