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Proceedings Paper

The concept models and implementations of multiport neural net associative memory for 2D patterns
Author(s): Vladimir G. Krasilenko; Aleksandr I. Nikolskyy; Rimma A. Yatskovskaya; Victor I. Yatskovsky
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Paper Abstract

The paper considers neural net models and training and recognizing algorithms with base neurobiologic operations: p-step autoequivalence and non-equivalenc The Modified equivalently models (MEMs) of multiport neural net associative memory (MNNAM) are offered with double adaptive - equivalently weighing (DAEW) for recognition of 2D-patterns (images). It is shown, the computing process in MNNAM under using the proposed MEMs, is reduced to two-step and multi-step algorithms and step-by-step matrix-matrix (tensor-tensor) procedures. The given results of computer simulations confirmed the perspective of such models. Besides the result was received when MNNAM capacity on base of MEMs exceeded the amount of neurons.

Paper Details

Date Published: 26 April 2011
PDF: 12 pages
Proc. SPIE 8055, Optical Pattern Recognition XXII, 80550T (26 April 2011); doi: 10.1117/12.883669
Show Author Affiliations
Vladimir G. Krasilenko, Open International Univ. of Human Development (Ukraine)
Aleksandr I. Nikolskyy, Vinnitsa National Technical Univ. (Ukraine)
Rimma A. Yatskovskaya, Vinnitsa State Agrarian Univ. (Ukraine)
Victor I. Yatskovsky, Vinnitsa State Agrarian Univ. (Ukraine)

Published in SPIE Proceedings Vol. 8055:
Optical Pattern Recognition XXII
David P. Casasent; Tien-Hsin Chao, Editor(s)

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