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dc.contributor.authorAraki, Osamuen_US
dc.date.accessioned2011-11-14T18:19:23Z
dc.date.available2011-11-14T18:19:23Z
dc.date.issued1993-05en_US
dc.identifier.urihttp://hdl.handle.net/2144/2019
dc.description.abstractWe can recognize objects through receiving continuously huge temporal information including redundancy and noise, and can memorize them. This paper proposes a neural network model which extracts pre-recognized patterns from temporally sequential patterns which include redundancy, and memorizes the patterns temporarily. This model consists of an adaptive resonance system and a recurrent time-delay network. The extraction is executed by the matching mechanism of the adaptive resonance system, and the temporal information is processed and stored by the recurrent network. Simple simulations are examined to exemplify the property of extraction.en_US
dc.description.sponsorshipMatsushita Electric Industrial Co., Ltd., Tokyo Information Systems Research Laboratory, Tokyo, Japanen_US
dc.publisherBoston University Center for Adaptive Systems and Department of Cognitive and Neural Systemsen_US
dc.relation.ispartofseriesBU CAS/CNS Technical Reports;CAS/CNS-TR-1993-042en_US
dc.rightsCopyright 1993 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and / or special permission.en_US
dc.titleExtracted Memory from Temporal Patterns Using Adaptive Resonance and Recurrent Networken_US
dc.typeTechnical Reporten_US
dc.rights.holderBoston University Trusteesen_US


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