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dc.contributor.authorHSU, KYen_US
dc.contributor.authorLIN, SHen_US
dc.date.accessioned2014-12-08T15:03:36Z-
dc.date.available2014-12-08T15:03:36Z-
dc.date.issued1995en_US
dc.identifier.issn0143-8166en_US
dc.identifier.urihttp://hdl.handle.net/11536/2137-
dc.description.abstractPrinciples of the photorefractive perceptron learning algorithm are described. The influences of the finite response time and hologram erasure of the photorefractive gratings on the convergence property of the photorefractive perceptron learning are discussed. A novel neural network which could resolve these constraints is presented. It is a hybrid system which utilizes the photorefractive holographic gratings to implement the inner product between the input image and the interconnection matrix. A personal computer is used for storing the interconnection matrix and the updating procedure, and it also functions as a feedback means during the learning phase. After training the weight vectors are recorded in the volume hologram of an optical processor. This novel method combines the advantages of the massive parallelism of optical systems and the programmability of electronic computers. Experimental results of image classification are presented. It shows that the system could correctly classify the input patterns into one of the two groups after training on four examples in each group during successive iterations. The system has been extended to perform multi-category image classification.en_US
dc.language.isoen_USen_US
dc.titleA HYBRID NEURAL-NETWORK FOR IMAGE CLASSIFICATIONen_US
dc.typeArticleen_US
dc.identifier.journalOPTICS AND LASERS IN ENGINEERINGen_US
dc.citation.volume23en_US
dc.citation.issue2-3en_US
dc.citation.spage167en_US
dc.citation.epage183en_US
dc.contributor.department電子物理學系zh_TW
dc.contributor.department光電工程學系zh_TW
dc.contributor.departmentDepartment of Electrophysicsen_US
dc.contributor.departmentDepartment of Photonicsen_US
dc.identifier.wosnumberWOS:A1995RQ73000008-
dc.citation.woscount1-
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