Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7694
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dc.contributor.authorJi, Han-Bingen_US
dc.contributor.authorProf. LEUNG Kwong Saken_US
dc.contributor.authorLeung, Yeeen_US
dc.date.accessioned2023-03-30T05:26:33Z-
dc.date.available2023-03-30T05:26:33Z-
dc.date.issued1996-
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1996, vol. 1112 LNCS, pp. 21 - 27en_US
dc.identifier.isbn3540615105-
dc.identifier.isbn978-354061510-1-
dc.identifier.issn03029743-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/7694-
dc.description.abstractA 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.en_US
dc.language.isoenen_US
dc.publisherSpringer Verlagen_US
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.titleA novel encoding strategy for associative memoryen_US
dc.typeConference Paperen_US
dc.identifier.doi10.1007/3-540-61510-5_8-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
Appears in Collections:Applied Data Science - Publication
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