Enhanced RBF Network by Using ART2 Algorithm and Fuzzy Control Method(<Special Section>Papers Selected from ITC-CSCC 2004)

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概要

• 論文の詳細を見る

• This paper proposes an enhanced RBF network that enhances learning algorithms between input layer and middle layer and between middle layer and output layer individually for improving the efficiency of learning. The proposed network applies ART2 network as the learning structure between input layer and middle layer. And the auto-tuning method of learning rate and momentum is proposed and applied to learning between middle layer and output layer, which arbitrates learning rate and momentum dynamically by using the fuzzy control system for the arbitration of the connected weight between middle layer and output layer. The experiment for the classification of number patterns extracted from the citizen registration card shows that compared with conventional networks such as delta-bar-delta algorithm and the ART2-based RBF network, the proposed method achieves the improvement of performance in terms of learning speed and convergence.

• 社団法人電子情報通信学会の論文
• 2005-06-01

著者

• Je Sung-kwan

  Department Of Computer Science Pusan National University

• Kim Young‐ju

  Sogang Univ. Seoul Kor

• Kim Young-ju

  Department Of Computer Engineering Silla University

• KIM Kwang-Baek

  Department of Computer Engineering, Silla University

• Kim Kwang‐baek

  Silla Univ. Busan Kor

• Kim Kwang-baek

  Department Of Computer Engineering Silla University

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