{"title":"Fuzzy c-regression models based on the BELS method for nonlinear system identification","authors":"B. Aissaoui, M. Soltani, D. Elleuch, A. Chaari","doi":"10.1109/ICEESA.2013.6578425","DOIUrl":null,"url":null,"abstract":"A fuzzy c-regression model clustering algorithm based on Bias-Eliminated Least Squares method (BELS) is presented. This method is designed to develop an identification procedure for noisy nonlinear systems. The BELS method is used to identify consequent parameters and eliminate the bias. The proposed approach has been applied to benchmark modeling problem which proved a good performance.","PeriodicalId":212631,"journal":{"name":"2013 International Conference on Electrical Engineering and Software Applications","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Conference on Electrical Engineering and Software Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEESA.2013.6578425","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
A fuzzy c-regression model clustering algorithm based on Bias-Eliminated Least Squares method (BELS) is presented. This method is designed to develop an identification procedure for noisy nonlinear systems. The BELS method is used to identify consequent parameters and eliminate the bias. The proposed approach has been applied to benchmark modeling problem which proved a good performance.
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