JICCE (한국정보통신학회)
Current Result Document :
| 한글제목(Korean Title) |
Enhanced Fuzzy Single Layer Perceptron |
| 영문제목(English Title) |
Enhanced Fuzzy Single Layer Perceptron |
| 저자(Author) |
Gyoo-Yong Chae
Sang-Hee Eom
Kwang-Baek Kim
|
| 원문수록처(Citation) |
VOL 02 NO. 01 PP. 0036 ~ 0039 (2004. 03) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
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. |
| 키워드(Keyword) |
XOR problem
learning speed
convergence rate
pattern recognition
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| 파일첨부 |
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