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Proceedings Paper

Vector neural net identifying many strongly distorted and correlated patterns
Author(s): Boris V. Kryzhanovsky; Andrei L. Mikaelian; Anatoly B. Fonarev
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Paper Abstract

We suggest an effective and simple algorithm providing a polynomial storage capacity of a network of the form M ~ N2s+1, where N is the dimension of the stored binary patterns. In this problem the value of the free parameter s is restricted by the inequalities N >> slnN ≥ 1. The algorithm allows us to identify a large number of highly distorted similar patterns. The negative influence of correlations of the patterns is suppressed by choosing a sufficiently large value of the parameter s. We show the efficiency of the algorithm by the example of a perceptron identifier, but it also can be used to increase the storage capacity of full connected systems of associative memory.

Paper Details

Date Published: 11 January 2005
PDF: 10 pages
Proc. SPIE 5642, Information Optics and Photonics Technology, (11 January 2005); doi: 10.1117/12.572334
Show Author Affiliations
Boris V. Kryzhanovsky, Institute of Optical Neural Technologies (Russia)
Andrei L. Mikaelian, Institute of Optical Neural Technologies (Russia)
Anatoly B. Fonarev, CUNY/College of Staten Island (United States)


Published in SPIE Proceedings Vol. 5642:
Information Optics and Photonics Technology
Guoguang Mu; Francis T. S. Yu; Suganda Jutamulia, Editor(s)

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