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Proceedings Paper

Criterion for correct recalls in associative-memory neural networks
Author(s): Han-Bing Ji
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Paper Abstract

A novel weighted outer-product learning (WOPL) scheme for associative memory neural networks (AMNNs) is presented. In the scheme, each fundamental memory is allocated a learning weight to direct its correct recall. Both the Hopfield and multiple training models are instances of the WOPL model with certain sets of learning weights. A necessary condition of choosing learning weights for the convergence property of the WOPL model is obtained through neural dynamics. A criterion for choosing learning weights for correct associative recalls of the fundamental memories is proposed. In this paper, an important parameter called signal to noise ratio gain (SNRG) is devised, and it is found out empirically that SNRGs have their own threshold values which means that any fundamental memory can be correctly recalled when its corresponding SNRG is greater than or equal to its threshold value. Furthermore, a theorem is given and some theoretical results on the conditions of SNRGs and learning weights for good associative recall performance of the WOPL model are accordingly obtained. In principle, when all SNRGs or learning weights chosen satisfy the theoretically obtained conditions, the asymptotic storage capacity of the WOPL model will grow at the greatest rate under certain known stochastic meaning for AMNNs, and thus the WOPL model can achieve correct recalls for all fundamental memories. The representative computer simulations confirm the criterion and theoretical analysis.

Paper Details

Date Published: 16 December 1992
PDF: 12 pages
Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); doi: 10.1117/12.130870
Show Author Affiliations
Han-Bing Ji, Shanghai Jiao Tong Univ. (Hong Kong)

Published in SPIE Proceedings Vol. 1766:
Neural and Stochastic Methods in Image and Signal Processing
Su-Shing Chen, Editor(s)

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