{"title":"Variational Bayesian modified adaptive mamdani fuzzy modelling for use in condition monitoring","authors":"Yu Zhang, Jun Chen, C. Bingham, T. Gordon","doi":"10.1109/CIVEMSA.2015.7158608","DOIUrl":null,"url":null,"abstract":"The paper proposes a new Adaptive Mamdani Fuzzy Model (AMFM) based system modelling methodology that improves on traditional Mamdani fuzzy rule based system (FRBS) techniques through use of alternative membership functions and a defuzzification mechanism that is `differentiable', allowing a back error propagation (BEP) algorithm to refine the initial fuzzy model. Moreover, a variational Bayesian (VB) method is applied to simplify the results via automatic selection of the number of input rules so that redundant rules can be removed for the initial modelling phase. The efficacy of the proposed VB modified AMFM (VB-AMFM) approach is demonstrated through experimental trials using measurements from a compressor in an industrial gas turbine (IGT).","PeriodicalId":348918,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIVEMSA.2015.7158608","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The paper proposes a new Adaptive Mamdani Fuzzy Model (AMFM) based system modelling methodology that improves on traditional Mamdani fuzzy rule based system (FRBS) techniques through use of alternative membership functions and a defuzzification mechanism that is `differentiable', allowing a back error propagation (BEP) algorithm to refine the initial fuzzy model. Moreover, a variational Bayesian (VB) method is applied to simplify the results via automatic selection of the number of input rules so that redundant rules can be removed for the initial modelling phase. The efficacy of the proposed VB modified AMFM (VB-AMFM) approach is demonstrated through experimental trials using measurements from a compressor in an industrial gas turbine (IGT).
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