T. Arnould, S. Tano, T. Miyoshi, Y. Kato, T. Oyama, A. Bastian, M. Umano
{"title":"Algorithms for fuzzy inference and tuning in the fuzzy inference software FINEST","authors":"T. Arnould, S. Tano, T. Miyoshi, Y. Kato, T. Oyama, A. Bastian, M. Umano","doi":"10.1109/FUZZY.1995.409811","DOIUrl":null,"url":null,"abstract":"In this paper, we explain the algorithms used in FINEST, the Fuzzy Inference Environment Software with Tuning developed at LIFE (Laboratory for International Fuzzy Engineering Research). The research themes and associated algorithms were defined to palliate the insufficiencies of usual inference methods and come naturally from the formulation of fuzzy \"if... then...\" rules. In particular, enhanced versions of combination operators, implication functions and aggregation operators are proposed, as well as a mechanism to tune the parameters used in the definition of the knowledge used in the system. Finally, one definition and formulation of backward reasoning with fuzzy \"if... then...\" rules is proposed.<<ETX>>","PeriodicalId":150477,"journal":{"name":"Proceedings of 1995 IEEE International Conference on Fuzzy Systems.","volume":"103 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 1995 IEEE International Conference on Fuzzy Systems.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FUZZY.1995.409811","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
In this paper, we explain the algorithms used in FINEST, the Fuzzy Inference Environment Software with Tuning developed at LIFE (Laboratory for International Fuzzy Engineering Research). The research themes and associated algorithms were defined to palliate the insufficiencies of usual inference methods and come naturally from the formulation of fuzzy "if... then..." rules. In particular, enhanced versions of combination operators, implication functions and aggregation operators are proposed, as well as a mechanism to tune the parameters used in the definition of the knowledge used in the system. Finally, one definition and formulation of backward reasoning with fuzzy "if... then..." rules is proposed.<>