11 January 2005 Vector neural net identifying many strongly distorted and correlated patterns
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Proceedings Volume 5642, Information Optics and Photonics Technology; (2005); doi: 10.1117/12.572334
Event: Photonics Asia, 2004, Beijing, China
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.
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Boris V. Kryzhanovsky, Andrei L. Mikaelian, Anatoly B. Fonarev, "Vector neural net identifying many strongly distorted and correlated patterns", Proc. SPIE 5642, Information Optics and Photonics Technology, (11 January 2005); doi: 10.1117/12.572334; https://doi.org/10.1117/12.572334
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KEYWORDS
Neural networks

Binary data

Neurons

Content addressable memory

Detection and tracking algorithms

Physics

Reliability

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