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Design and simulation of a multiport neural network heteroassociative memory for optical pattern recognitions
Author(s): Vladimir Krasilenko; Alexander Lazarev; Sveta Grabovlyak
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Paper Abstract

The modified matrix equivalently models (MMEMs) of multiport neural network heteroassociative memory (MP_NN_HAM) with double adaptive - equivalently weighing (DAEW) for recognition of 1D and 2D-patterns (images) are offered. It is shown, that computing process in MP_NN_HAM under using the proposed MMEMs, is reduced to two-step and multi-step algorithms and step-by-step matrix-matrix (tensor-tensor) procedures. The base operations and structural components for construction of MP_NN_HAM are matrix-matrix multipliers and matrixes of nonlinear converters, including threshold transformations. Advantages of such MMEMs for MP_NN_HAM were shown and confirmed by computer simulation results. The aim of paper is research of improved models and MP_NN_HAM for input 1D and 2D signals with unipolar coding and their capacity determination. The given results of computer simulations confirmed the perspective of such models. Results were also received for case of a MP_NN_HAM on base of MMEMs capacity exceeded a neurons amount. This memory is intended to recognize parallel and refresh P input distorted images (N-element vector). Such MP_NN_HAM is a kind of combination consisting of P independently functioning NN_HAM with common memory. Variants of optical realization of MP_NN_HAM architectures are considered in paper. A whole system is consists of two matrix-matrix (for 1D patterns) or two tensortensor (for 2D patterns) equivalentors (E) (or nonequivalentors (NE)) (MME and MMNE or TTE and TTNE).The proposed E (or NE) architecture with temporary integration has more large dimension of HAM and more simple design.

Paper Details

Date Published: 23 April 2012
PDF: 10 pages
Proc. SPIE 8398, Optical Pattern Recognition XXIII, 83980N (23 April 2012); doi: 10.1117/12.919837
Show Author Affiliations
Vladimir Krasilenko, Vinnitsa Social Economy Institute (Ukraine)
Alexander Lazarev, Vinnitsa National Technical Univ. (Ukraine)
Sveta Grabovlyak, Vinnitsa Social Economy Institute (Ukraine)

Published in SPIE Proceedings Vol. 8398:
Optical Pattern Recognition XXIII
David P. Casasent; Tien-Hsin Chao, Editor(s)

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