{"title":"Boil-Turbine System Identification Based on Robust Interval Type-2 Fuzzy C-Regression Model","authors":"J. Shi","doi":"10.1142/s1469026822500225","DOIUrl":null,"url":null,"abstract":"The boil-turbine system is a multivariable and strong coupling system with the characteristics of nonlinearity, time-varying parameters, and large delay. The accurate model can effectively improve the performance of turbine–boiler coordinated control system. In this paper, the boil-turbine model is established by interval type-2 (IT2) T-S fuzzy model. The premise parameters of IT2 T-S fuzzy model are identified by robust IT2 fuzzy c-regression model (RIT2-FCRM) clustering algorithm. The RIT2-FCRM is based on interval type-2 fuzzy sets (IT2FS) and applies a robust objective function, this clustering algorithm can reduce the impacts of outliers and noise points. The effectiveness and practicability of RIT2-FCRM are demonstrated by the identification results of the boiler–turbine system.","PeriodicalId":422521,"journal":{"name":"Int. J. Comput. Intell. Appl.","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Comput. Intell. Appl.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/s1469026822500225","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
The boil-turbine system is a multivariable and strong coupling system with the characteristics of nonlinearity, time-varying parameters, and large delay. The accurate model can effectively improve the performance of turbine–boiler coordinated control system. In this paper, the boil-turbine model is established by interval type-2 (IT2) T-S fuzzy model. The premise parameters of IT2 T-S fuzzy model are identified by robust IT2 fuzzy c-regression model (RIT2-FCRM) clustering algorithm. The RIT2-FCRM is based on interval type-2 fuzzy sets (IT2FS) and applies a robust objective function, this clustering algorithm can reduce the impacts of outliers and noise points. The effectiveness and practicability of RIT2-FCRM are demonstrated by the identification results of the boiler–turbine system.