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Proceedings Paper

Perfectly retrievable unipolar optoelectronic neural associative memory
Author(s): Chwan-Hwa Wu; Hua-Kuang Liu
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Paper Abstract

A perfectly convergent, unipolar, neural associative memory system based on nonlinear, dynamical terminal attractors is presented. By adaptively setting the threshold values for the dynamic iteration for the unipolar, binary neuron states with terminal-attractors, the achievement of perfect convergence and correct retrieval has been demonstrated via computer simulation. The simulations are completed by (1) exhaustive tests with all of the possible combinations of stored and test vectors in small-scale networks, and (2) Monte Carlo simulations with randomly generated stored and test vectors in large-scale networks with an M/N ratio equals 4 (M: the number of stored vectors; and N: the number of neurons up to 256). An experiment with exclusive-or logic operation using LCTV SLMs is used to show the feasibility of an optoelectronic implementation of the model. The behavior of terminal attractors in basins of energy space is illustrated through examples.

Paper Details

Date Published: 30 October 1992
PDF: 12 pages
Proc. SPIE 1812, Optical Computing and Neural Networks, (30 October 1992); doi: 10.1117/12.131200
Show Author Affiliations
Chwan-Hwa Wu, Auburn Univ. (United States)
Hua-Kuang Liu, Jet Propulsion Lab. (United States)

Published in SPIE Proceedings Vol. 1812:
Optical Computing and Neural Networks
Ken Yuh Hsu; Hua-Kuang Liu, Editor(s)

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