Title :
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
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;
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-1-4244-6526-2
DOI :
10.1109/ICMLC.2010.5580478
