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.
fDate :
March 1 2007-April 5 2007
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;
Conference_Titel :
Computational Intelligence and Data Mining, 2007. CIDM 2007. IEEE Symposium on
Conference_Location :
Honolulu, HI
Print_ISBN :
1-4244-0705-2
DOI :
10.1109/CIDM.2007.368878
