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