DocumentCode
389696
Title
A novel cluster method in fuzzy neural networks
Author
Li, De-Qiang ; Huang, Sha-Bai
Author_Institution
Inst. of Autom., Acad. Sinica, Shenyang, China
Volume
1
fYear
2002
fDate
2002
Firstpage
261
Abstract
Ching-chang Wong et. al(1999) proposed a cluster method to make training sample data stepwise converge to cluster centers regardless of the predetermination of center number. This paper improves the cluster method, and proves its convergence by using Brouwer fixed point theorem. Based on the result of the cluster method, one first order TSK fuzzy neural network is established and a hybrid algorithm is implemented to tune network parameters. Finally, simulation results are given to demonstrate the effectiveness of this cluster method in fuzzy neural networks.
Keywords
convergence; fuzzy neural nets; learning (artificial intelligence); pattern clustering; Brouwer fixed point theorem; Takagi-Sugeno-Kang neural network; cluster method; convergence; first order TSK fuzzy neural network; Clustering algorithms; Convergence; Fuzzy neural networks; Fuzzy set theory; Fuzzy sets; Fuzzy systems; Intelligent networks; Neural networks; Power system modeling; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2002. Proceedings. 2002 International Conference on
Print_ISBN
0-7803-7508-4
Type
conf
DOI
10.1109/ICMLC.2002.1176752
Filename
1176752
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