DocumentCode
464324
Title
On Obtaining Fuzzy Rule Base from Ensemble of Takagi-Sugeno Systems
Author
Korytkowski, Marcin ; Rutkowski, Leszek ; Scherer, Rafal ; Drozda, Grzegorz
Author_Institution
Dept. of Comput. Eng., Czestochowa Univ. of Technol.
fYear
2007
fDate
March 1 2007-April 5 2007
Firstpage
234
Lastpage
237
Abstract
Takagi-Sugeno fuzzy systems are very common learning systems. The paper is about building classification ensembles from them and merging resulting rule bases. When merged, the rule base is more intelligible and easier to process. The merging is possible thanks to a modification of TS systems. Numerical simulations show that the modified systems perform very well
Keywords
fuzzy systems; knowledge based systems; learning systems; Takagi-Sugeno fuzzy systems; Takagi-Sugeno system ensemble; classification ensembles; fuzzy rule base; learning systems; Backpropagation algorithms; Boosting; Computational intelligence; Computer science; Data mining; Fuzzy neural networks; Fuzzy systems; Learning systems; Merging; Takagi-Sugeno model;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Data Mining, 2007. CIDM 2007. IEEE Symposium on
Conference_Location
Honolulu, HI
Print_ISBN
1-4244-0705-2
Type
conf
DOI
10.1109/CIDM.2007.368878
Filename
4221302
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