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http://dx.doi.org/10.5391/IJFIS.2005.5.2.095

A New Learning Algorithm of Neuro-Fuzzy Modeling Using Self-Constructed Clustering  

Ryu, Jeong-Woong (School of Electrical & Computer Engineering, Chungbuk National University)
Song, Chang-Kyu (School of Electrical & Computer Engineering, Chungbuk National University)
Kim, Sung-Suk (School of Electrical & Computer Engineering, Chungbuk National University)
Kim, Sung-Soo (School of Electrical & Computer Engineering, Chungbuk National University)
Publication Information
International Journal of Fuzzy Logic and Intelligent Systems / v.5, no.2, 2005 , pp. 95-101 More about this Journal
Abstract
In this paper, we proposed a learning algorithm for the neuro-fuzzy modeling using a learning rule to adapt clustering. The proposed algorithm includes the data partition, assigning the rule into the process of partition, and optimizing the parameters using predetermined threshold value in self-constructing algorithm. In order to improve the clustering, the learning method of neuro-fuzzy model is extended and the learning scheme has been modified such that the learning of overall model is extended based on the error-derivative learning. The effect of the proposed method is presented using simulation compare with previous ones.
Keywords
Clustering; Neuro-Fuzzy Modeling; TSK Fuzzy Model; Self-Constructed Clustering; System Identification;
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