{"title":"A tuning method for fuzzy inference with fuzzy input and fuzzy output","authors":"T. Oyama, S. Tano, T. Arnould","doi":"10.1109/FUZZY.1994.343852","DOIUrl":null,"url":null,"abstract":"Most studies on tuning of fuzzy inference are concerned with numerical inputs and outputs only, and very few research has been done on tuning of fuzzy inference with fuzzy inputs and outputs. Moreover, in many cases the object of tuning are fuzzy predicates only, apart from the other factors intervening in fuzzy inference. In this paper the authors propose a method to tune the fuzzy inference when inputs and outputs are given as fuzzy sets. This method is similar to backpropagation and tunes the parameters of aggregation operators, implication functions and combination functions as well as the fuzzy predicates which appear in the nodes of the network representing the calculation process of the fuzzy inference. Some results of tuning simulation are also shown.<<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":"11","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.1994.343852","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Abstract
Most studies on tuning of fuzzy inference are concerned with numerical inputs and outputs only, and very few research has been done on tuning of fuzzy inference with fuzzy inputs and outputs. Moreover, in many cases the object of tuning are fuzzy predicates only, apart from the other factors intervening in fuzzy inference. In this paper the authors propose a method to tune the fuzzy inference when inputs and outputs are given as fuzzy sets. This method is similar to backpropagation and tunes the parameters of aggregation operators, implication functions and combination functions as well as the fuzzy predicates which appear in the nodes of the network representing the calculation process of the fuzzy inference. Some results of tuning simulation are also shown.<>
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