{"title":"An approach to inverse fuzzy arithmetic","authors":"M. Hanss","doi":"10.1109/NAFIPS.2003.1226831","DOIUrl":null,"url":null,"abstract":"A novel approach of inverse fuzzy arithmetic is introduced to successfully identify the uncertain parameters of linear and nonlinear models on the basis of uncertain values for the output variables of the model. The presented method is based on the transformation method, which has been proposed as a powerful tool for the simulation and analysis of systems with uncertain model parameters. A general scheme for the practical implementation of the inverse fuzzy-arithmetical approach is given, and the effectiveness of the method is shown for two examples, which consist of a linear model and a nonlinear model, respectively.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NAFIPS.2003.1226831","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
Abstract
A novel approach of inverse fuzzy arithmetic is introduced to successfully identify the uncertain parameters of linear and nonlinear models on the basis of uncertain values for the output variables of the model. The presented method is based on the transformation method, which has been proposed as a powerful tool for the simulation and analysis of systems with uncertain model parameters. A general scheme for the practical implementation of the inverse fuzzy-arithmetical approach is given, and the effectiveness of the method is shown for two examples, which consist of a linear model and a nonlinear model, respectively.