한국산학기술학회:학술대회논문집 (Proceedings of the KAIS Fall Conference)
- 한국산학기술학회 2003년도 Proceeding
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- Pages.84-87
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- 2003
Enhanced RBF Network by Using Auto- Turning Method of Learning Rate, Momentum and ART2
- Kim, Kwang-baek (Dept. of Computer Engineering, Silla University) ;
- Moon, Jung-wook (Dept. of Computer Engineering, Busan National University)
- 발행 : 2003.11.01
초록
This paper proposes the enhanced REF network, which arbitrates learning rate and momentum dynamically by using the fuzzy system, to arbitrate the connected weight effectively between the middle layer of REF network and the output layer of REF network. ART2 is applied to as the learning structure between the input layer and the middle layer and the proposed auto-turning method of arbitrating the learning rate as the method of arbitrating the connected weight between the middle layer and the output layer. The enhancement of proposed method in terms of learning speed and convergence is verified as a result of comparing it with the conventional delta-bar-delta algorithm and the REF network on the basis of the ART2 to evaluate the efficiency of learning of the proposed method.