G-fuzzy ART: a geometrical fuzzy ART neural network architecture

Issam J. Dagher

doi:10.1117/12.488160

1 April 2003 G-fuzzy ART: a geometrical fuzzy ART neural network architecture

Issam J. Dagher

Proceedings Volume 5102, Independent Component Analyses, Wavelets, and Neural Networks; (2003) https://doi.org/10.1117/12.488160
Event: AeroSense 2003, 2003, Orlando, Florida, United States

Abstract

In this paper, a geometrical Fuzzy ART (G-Fuzzy ART) neural network architecture is presented. While the original Fuzzy ART requires preprocessing of the input patterns (complement coding), the G-Fuzzy ART accept the input patterns without complement coding. The weights of the G-Fuzzy ART refer directly to the borders of the hyper-rectangle while the weights in the Fuzzy ART refer to the endpoints of the hyper-rectangle. The size of the hyper-rectangle is directly given by the size of the weight. The geometrical choice function (the Hamming distance of the input pattern to the hyper-rectangle) and the weight update formulas for the G-Fuzzy ART are presented. The G-Fuzzy ART retains the notion of resonance by retaining the vigilance criterion applied directly to the new size of the hyper-rectangle. It also retains the min-max fuzzy operators.

Citation Download Citation

Issam J. Dagher "G-fuzzy ART: a geometrical fuzzy ART neural network architecture", Proc. SPIE 5102, Independent Component Analyses, Wavelets, and Neural Networks, (1 April 2003); https://doi.org/10.1117/12.488160

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