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Proceedings Paper

G-fuzzy ART: a geometrical fuzzy ART neural network architecture
Author(s): Issam J. Dagher
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Paper 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.

Paper Details

Date Published: 1 April 2003
PDF: 10 pages
Proc. SPIE 5102, Independent Component Analyses, Wavelets, and Neural Networks, (1 April 2003); doi: 10.1117/12.488160
Show Author Affiliations
Issam J. Dagher, Univ. of Balamand (Lebanon)


Published in SPIE Proceedings Vol. 5102:
Independent Component Analyses, Wavelets, and Neural Networks
Anthony J. Bell; Mladen V. Wickerhauser; Harold H. Szu, Editor(s)

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