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Enhanced Fuzzy Single Layer Perceptron
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  • Enhanced Fuzzy Single Layer Perceptron
  • Enhanced Fuzzy Single Layer Perceptron
저자명
Chae. Gyoo-Yong,Eom. Sang-Hee,Kim. Kwang-Baek
간행물명
International journal of maritime information and communication sciences
권/호정보
2004년|2권 1호|pp.36-39 (4 pages)
발행정보
한국해양정보통신학회
파일정보
정기간행물|ENG|
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기타
이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
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기타언어초록

In this paper, a method of improving the learning speed and convergence rate is proposed to exploit the advantages of artificial neural networks and neuro-fuzzy systems. This method is applied to the XOR problem, n bit parity problem, which is used as the benchmark in the field of pattern recognition. The method is also applied to the recognition of digital image for practical image application. As a result of experiment, it does not always guarantee convergence. However, the network showed considerable improvement in learning time and has a high convergence rate. The proposed network can be extended to any number of layers. When we consider only the case of the single layer, the networks had the capability of high speed during the learning process and rapid processing on huge images.