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CONDITIONAL CONVERGENCE OF PHOTOREFRACTIVE PERCEPTRON LEARNING

dc.citation.epage2137en_US
dc.citation.issue24en_US
dc.citation.spage2135en_US
dc.citation.volume18en_US
dc.citation.woscount7
dc.contributor.authorHSU, KYen_US
dc.contributor.authorLIN, SHen_US
dc.contributor.authorYEH, Pen_US
dc.contributor.department光電工程學系zh_TW
dc.contributor.departmentDepartment of Photonicsen_US
dc.date.accessioned2014-12-08T15:04:14Z
dc.date.available2014-12-08T15:04:14Z
dc.date.issued1993-12-15en_US
dc.description.abstractWe consider the convergence characteristics of a perceptron learning algorithm, taking into account the decay of photorefractive holograms during the process of interconnection weight changes. As a result of the hologram erasure, the convergence of the learning process is dependent on the exposure time during the weight changes. A mathematical proof of the conditional convergence, as well as computer simulations of the photorefractive perceptrons, is presented and discussed.en_US
dc.identifier.doi10.1364/OL.18.002135en_US
dc.identifier.issn0146-9592en_US
dc.identifier.journalOPTICS LETTERSen_US
dc.identifier.urihttp://dx.doi.org/10.1364/OL.18.002135en_US
dc.identifier.urihttps://ir.lib.nycu.edu.tw/handle/11536/2743
dc.identifier.wosnumberWOS:A1993MP68400017
dc.language.isoen_USen_US
dc.titleCONDITIONAL CONVERGENCE OF PHOTOREFRACTIVE PERCEPTRON LEARNINGen_US
dc.typeArticleen_US

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