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Enhanced Fuzzy Single Layer Perceptron  

Chae, Gyoo-Yong (IT Design Research Center, Silla University)
Eom, Sang-Hee (School of Computer Information and Communication, Dongju Colleg)
Kim, Kwang-Baek (Computer Engineering Department, Silla University)
Publication Information
Journal of information and communication convergence engineering / v.2, no.1, 2004 , pp. 36-39 More about this Journal
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.
Keywords
XOR problem; learning speed; convergence rate; pattern recognition;
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