FCM clustering algorithm for T-S fuzzy model identification

Author

Han, Pu ; Shi, Jian-zhong ; Wang, Dong-feng ; Jiao, Song-ming

Author_Institution

Dept. of Autom., North China Electr. Power Univ., Baoding, China

Volume

2

fYear

2010

fDate

11-14 July 2010

Firstpage

563

Lastpage

566

Abstract

An approach for building T-S fuzzy model is proposed based on fuzzy c-mean clustering algorithm on the basis of nonlinear modeling experience. An alternative T-S fuzzy model is adapted, which has the uniformed premise structure, the premise parameter is decided by fuzzy c-mean clustering algorithm and the consequence parameters is calculated by least square algorithm, and the identification precision is enhanced. Finally the effectiveness and practicability of this method is demonstrated by the simulation result of Box-Jenkins gas furnace data and Mackey-Glass chaos time series.

Keywords

fuzzy logic; least squares approximations; time series; Box-Jenkins gas furnace data; FCM clustering algorithm; Mackey-Glass chaos time series; T-S fuzzy model identification; fuzzy c-mean clustering algorithm; least square algorithm; nonlinear modeling; uniformed premise structure; Adaptation model; Clustering algorithms; Data models; Fuzzy sets; Mathematical model; Predictive models; Solid modeling; Fuzzy c-mean; Fuzzy identification; Least square; T-S fuzzy model;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics (ICMLC), 2010 International Conference on

Conference_Location

Qingdao

Print_ISBN

978-1-4244-6526-2

Type

conf

DOI

10.1109/ICMLC.2010.5580478

Filename

5580478