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Proceedings Paper

Phase space structure of neural networks for pattern recognition
Author(s): Eugene I. Shubnikov
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Paper Abstract

The investigation of phase space structure of neural networks based on image correlation for pattern recognition is presented. The network has analog input and attractors as stationary points. Correlation of patterns is taken into consideration. Input images are spatially separated and they are represented as a stochastic field. Correlation theory is used to receive the analytical expressions. The maximum depth and width of a true attractor, the depth of false attractors, and the depth of a true attractor in comparison with false ones are received.

Paper Details

Date Published: 21 January 1994
PDF: 7 pages
Proc. SPIE 2051, International Conference on Optical Information Processing, (21 January 1994); doi: 10.1117/12.166066
Show Author Affiliations
Eugene I. Shubnikov, S. I. Vavilov State Optical Institute (Russia)

Published in SPIE Proceedings Vol. 2051:
International Conference on Optical Information Processing
Yuri V. Gulyaev; Dennis R. Pape, Editor(s)

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