9 Scopus citations

Abstract

Most models of Bidirectional associative memories intend to achieve that all trained pattern correspond to stable states; however, this has not been possible. Also, none of the former models has been able to recall all the trained patterns. In this work we introduce a new model of bidirectional associative memory which is not iterative and has no stability problems. It is based on the Alpha-Beta associative memories. This model allows, besides correct recall of noisy patterns, perfect recall of all trained patterns, with no ambiguity and no conditions. An example of fingerprint recognition is presented.

Original languageEnglish
Title of host publicationComputer and Information Sciences - ISCIS 2006
Subtitle of host publication21th International Symposium, Proceedings
PublisherSpringer Verlag
Pages286-295
Number of pages10
ISBN (Print)3540472428, 9783540472421
DOIs
StatePublished - 2006
EventISCIS 2006: 21th International Symposium on Computer and Information Sciences - Istanbul, Turkey
Duration: 1 Nov 20063 Nov 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4263 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceISCIS 2006: 21th International Symposium on Computer and Information Sciences
Country/TerritoryTurkey
CityIstanbul
Period1/11/063/11/06

Keywords

  • Alpha-Beta associative memories
  • Bidirectional associative memories
  • Perfect recall

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