Title :
Fuzzy modeling by hyperbolic fuzzy k-means clustering
Author_Institution :
Dept. of Ind. & Syst. Eng., Chuo Univ., Tokyo, Japan
fDate :
6/24/1905 12:00:00 AM
Abstract :
A parameterized Takagi-Sugeno´s model is proposed by introducing the classification function used in the hyperbolic fuzzy k-means method, and an identification procedure of this model is presented by applying the hyperbolic fuzzy k-means
Keywords :
fuzzy set theory; identification; nonlinear systems; pattern clustering; Takagi-Sugeno model; fuzzy clustering; fuzzy model; fuzzy set theory; hyperbolic fuzzy k-means method; identification; nonlinear system; parametrization; Clustering methods; Equations; Fuzzy sets; Fuzzy systems; Input variables; Principal component analysis; Systems engineering and theory; Takagi-Sugeno model;
Conference_Titel :
Fuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7280-8
DOI :
10.1109/FUZZ.2002.1006733
