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Advance Neuro-Fuzzy Modeling Using a New Clustering Algorithm  

김승석 (충북대학교 전기공학과)
김성수 (충북대 정보통신연구)
유정웅 (충북대 정보통신연구소)
Publication Information
The Transactions of the Korean Institute of Electrical Engineers D / v.53, no.7, 2004 , pp. 536-543 More about this Journal
Abstract
In this paper, we proposed a new method of modeling a neuro-fuzzy system using a hybrid clustering algorithm. The initial parameters and the number of clusters of the proposed system are optimally chosen simultaneously with respect to the process of regression, which is a unique characteristics of the proposed system. The proposed algorithm presented in this work improves the overall performance of the proposed a neuro-fuzzy system by choosing a proper number of clusters adaptively according the characteristics of given data. The process of clustering is performed by deciding on the number of classes, which yields the property of convergence of the system. In experiments, the superiority of the proposed neuro-fuzzy system is demonstrated, especially the process of optimizing parameters and clustering of learning speed.
Keywords
Gaussian Mixture Model; Chen Clustering; TSK Fuzzy model; Neuro-Fuzzy System;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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