Title : 
T-S fuzzy affine linear modeling algorithm by possibilistic c-regression models clustering algorithm
         
        
            Author : 
Chung-Chun Kung ; Hong-Chi Ku
         
        
            Author_Institution : 
Dept. of Electr. Eng., Tatung Univ., Taipei, Taiwan
         
        
        
        
        
        
            Abstract : 
This paper presents a Takagi-Sugeno (T-S) fuzzy affine linear modeling algorithm by the possibilistic c-regression models (PCRM) clustering algorithm. We apply the PCRM to partition the given input-output data into hyper-plane-shaped clusters (regression models). We choose the suitable number of cluster by the cluster validity criterion and then to construct the T-S fuzzy affine linear model. A simulation example is provided to demonstrate the effectiveness of the T-S fuzzy affine linear modeling algorithm.
         
        
            Keywords : 
fuzzy set theory; pattern clustering; possibility theory; regression analysis; PCRM clustering algorithm; T-S fuzzy affine linear modeling algorithm; Takagi-Sugeno fuzzy affine linear modeling algorithm; hyper-plane-shaped clusters; input-output data; possibilistic c-regression model clustering algorithm; Clustering algorithms; Conferences; Data models; Fuzzy systems; Nonlinear systems; Partitioning algorithms; Takagi-Sugeno model; Takagi-Sugeno (T-S) fuzzy model; affine linear; cluster validity criterion; possibilistic c-regression models (PCRM);
         
        
        
        
            Conference_Titel : 
Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference on
         
        
            Conference_Location : 
Beijing
         
        
            Print_ISBN : 
978-1-4799-2073-0
         
        
        
            DOI : 
10.1109/FUZZ-IEEE.2014.6891768