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A Hybrid RBF Network based on Fuzzy Dynamic Learning Rate Control

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2014, 19(9), pp.33-38

Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science

Kim, Kwang-baek ¹, Park Choong Shik ²

¹신라대학교
²영동대학교

ABSTRACT
열림/닫힘 버튼

The FCM based hybrid RBF network is a heterogeneous learning network model that appliesFCM algorithm between input and middle layer and applies Max_Min algorithm between middlelayer and output. The Max-Min neural network uses winner nodes of the middle layer as input butshows inefficient learning in performance when the input vector consists of too many patterns. To overcome this problem, we propose a dynamic learning rate control based on fuzzy logic. Theproposed method first classifies accurate/inaccurate class with respect to the difference betweentarget value and output value with threshold and then fuzzy membership function and fuzzydecision logic is designed to control the learning rate dynamically. We apply this proposed RBFnetwork to the character recognition problem and the efficacy of the proposed method is verifiedin the experiment.
KEYWORDS
열림/닫힘 버튼

FCMbased hybrid network, Max-Min neural network, Fuzzy logic, Learning rate

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열림/닫힘 버튼
* 2021년 이후 발행 논문의 참고문헌은 현재 구축 중입니다.

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