Part family formation through fuzzy ART2 neural network

Author(s): Chen, K.Y. | Chiu, C.Y. | Kuo, R.J. | Su, Y.T. | Tien, F.C. |

Year: 2006

Citation: DECISION SUPPORT SYSTEMS Volume: 42 Issue: 1 Pages: 89-103

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.

Topics: Image Analysis, Applications: Other, Models: ART 2 / Fuzzy ART, Self Organizing Maps,

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Cross References

  1. ART 2: Self organization of stable category recognition codes for analog input patterns
    Adaptive resonance architectures are neural networks that self-organize stable pattern recognition codes in real-time in response to arbitrary sequences of input patterns. This article introduces ART 2, a class of adaptive ... Article Details