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|Title:||G-fuzzy ART : a geometrical fuzzy ART neural network architecture||Authors:||Dagher, Issam||Affiliations:||Department of Computer Engineering||Issue Date:||2003||Part of:||Independent Component Analyses, Wavelets, and Neural Networks||Volume:||5102||Conference:||Independent Component Analyses, Wavelets, and Neural Networks (21-25 April 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. © (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
|Appears in Collections:||Department of Computer Engineering|
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