Title :
Type-2 Takagi-Sugeno-Kang Fuzzy Logic Modeling using Subtractive Clustering
Author :
Ren, Qun ; Baron, Luc ; Balazinski, Marek
Author_Institution :
Dept. of Mech. Eng., Ecole Polytech. de Montreal, Que.
Abstract :
In this paper, a subtractive clustering identification algorithm is introduced to model type-2 Takagi-Sugeno-Kang (TSK) fuzzy logic systems (FLS). The type-2 TSK FLS identification algorithm is an extension of the type-1 TSK FLS modeling algorithm proposed in (S. L. Chiu, 1994), (S. L. Chiu, 1997). In the type-2 algorithm, subtractive clustering method is combined with least squares estimation algorithms to pre-identify a type-1 FLS form input/output data. Then using type-2 TSK FLS theory (J. M. Mendel, 2001), expand the type-1 FLS to a type-2 TSK FLS. Minimum error models are obtained through enumerative search of optimum values for spreading percentage of cluster centers and consequence parameters. By doing so, fuzzy modeling of type-2 TSK FLS is found to be more effective than that of type-1 TSK FLS. Experimental results confirm the effectiveness of this method. A comparison of the Type-1 and -2 TSK FLSs is presented and the limitations of this method are discussed
Keywords :
fuzzy logic; fuzzy systems; least squares approximations; pattern clustering; Takagi- Sugeno-Kang fuzzy logic modeling; least squares estimation algorithms; minimum error models; subtractive clustering identification; Clustering algorithms; Clustering methods; Fuzzy logic; Fuzzy sets; Fuzzy systems; Least squares approximation; Proposals; System identification; Takagi-Sugeno-Kang model; Uncertainty;
Conference_Titel :
Fuzzy Information Processing Society, 2006. NAFIPS 2006. Annual meeting of the North American
Conference_Location :
Montreal, Que.
Print_ISBN :
1-4244-0363-4
Electronic_ISBN :
1-4244-0363-4
DOI :
10.1109/NAFIPS.2006.365871
