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Adaptive weighted outer-product learning associative memory

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3 Author(s)
Kwong-Sak Leung ; Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Shatin, Hong Kong ; Han-Bing Ji ; Yee Leung

Associative-memory neural networks with adaptive weighted outer-product learning are proposed in this paper. For the correct recall of a fundamental memory (FM), a corresponding learning weight is attached and a parameter called signal-to-noise-ratio-gain (SNRG) is devised. The sufficient conditions for the learning weights and the SNRG's are derived. It is found both empirically and theoretically that the SNRG's have their own threshold values for correct recalls of the corresponding FM's. Based on the gradient-descent approach, several algorithms are constructed to adaptively find the optimal learning weights with reference to global- or local-error measure

Published in:

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)  (Volume:27 ,  Issue: 3 )