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

fYear

2014

fDate

6-11 July 2014

Firstpage

1242

Lastpage

1247

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);

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4799-2073-0

Type

conf

DOI

10.1109/FUZZ-IEEE.2014.6891768

Filename

6891768