Part family formation through fuzzy ART2 neural network

Authors:
R. J. Kuo;Y. T. Su;C. Y. Chiu;Kai-Ying Chen;F. C. Tien
Affiliations:
Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei, Taiwan, Province of China;Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei, Taiwan, Province of China;Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei, Taiwan, Province of China;Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei, Taiwan, Province of China;Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei, Taiwan, Province of China
Venue:
Decision Support Systems
Year:
2006

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Abstract

In order to overcome some unavoidable factors, like shift of the part, that influence the crisp neural networks' recognition, the present study is dedicated in developing a novel fuzzy neural network (FNN), which integrates both the fuzzy set theory and adaptive resonance theory 2 (ART2) neural network for grouping the parts into several families based on the image captured from the Vision sensor. The proposed network posses the fuzzy inputs as well as the fuzzy weights. The model evaluation results showed that the proposed fuzzy neural network is able to provide more accurate results compared to the fuzzy self-organizing feature maps (SOM) neural network [R.J. Kuo, S.S. Chi, P.W. Teng, Generalized part family formation through fuzzy self-organizing feature map neural network, International Journal of Computers in Industrial Engineering, 40 (2001b) 79-100] and fuzzy c-means algorithm.