Please use this identifier to cite or link to this item:
Title: Adaptive weighted outer-product learning associative memory
Authors: Prof. LEUNG Kwong Sak 
Ji, Han-Bing 
Leung, Yee 
Issue Date: 1997
Source: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 1997, vol. 27 (3), pp. 533 - 543
Journal: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 
Abstract: 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. © 1997 IEEE.
Type: Peer Reviewed Journal Article
ISSN: 10834419
DOI: 10.1109/3477.584961
Appears in Collections:Publication

Show full item record


checked on Dec 5, 2023

Page view(s)

checked on Dec 5, 2023

Google ScholarTM

Impact Indices




Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.