Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7694
Title: A novel encoding strategy for associative memory
Authors: Ji, Han-Bing 
Prof. LEUNG Kwong Sak 
Leung, Yee 
Issue Date: 1996
Publisher: Springer Verlag
Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1996, vol. 1112 LNCS, pp. 21 - 27
Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 
Abstract: A novel encoding strategy for neural associative memory is presented in this paper. Unlike the conventional pointwise outer-product rule used in the Hopfield-type associative memories, the proposed encoding method computes the connection weight between two neurons by summing up not only the products of the corresponding two bits of all fundamental memories but also the products of their neighboring bits. Theoretical results concerning stability and attractivity are given. It is found both theoretically and experimentally that the proposed encoding scheme is an ideal approach for making the fundamental memories fLxed points and maximizing the storage capacity which can be many times of the current limits.
Type: Conference Paper
URI: http://hdl.handle.net/20.500.11861/7694
ISBN: 3540615105
978-354061510-1
ISSN: 03029743
DOI: 10.1007/3-540-61510-5_8
Appears in Collections:Applied Data Science - Publication

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