Dynamic Bidirectional Associative Memory Using Chaotic Neurons

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dc.contributor.authorLee, Ju-Jangko
dc.date.accessioned2009-01-08T05:58:20Z-
dc.date.available2009-01-08T05:58:20Z-
dc.date.created2012-02-06-
dc.date.created2012-02-06-
dc.date.issued2000-12-
dc.identifier.citationARTIFICIAL LIFE AND ROBOTICS, v.4, no.1, pp.12 - 16-
dc.identifier.issn1433-5298-
dc.identifier.urihttp://hdl.handle.net/10203/8246-
dc.descriptionThis work was presented, in part, at the Fourth International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–22, 1999en
dc.description.abstractA dynamic bidirectional associative memory (DBAM) with chaotic neurons as nodes is proposed. A learning algorithm based on Pontryagin’s minimum principle makes the DBAM equivalent to any other BAM so far reported. The input selection mechanism gives the DBAM the additional ability of multiple memory access, which is based on the dynamics of the chaotic neuron.-
dc.languageEnglish-
dc.language.isoen_USen
dc.publisherSpringer Verlag-
dc.titleDynamic Bidirectional Associative Memory Using Chaotic Neurons-
dc.typeArticle-
dc.type.rimsART-
dc.citation.volume4-
dc.citation.issue1-
dc.citation.beginningpage12-
dc.citation.endingpage16-
dc.citation.publicationnameARTIFICIAL LIFE AND ROBOTICS-
dc.embargo.liftdate9999-12-31-
dc.embargo.terms9999-12-31-
dc.contributor.localauthorLee, Ju-Jang-
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