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dc.contributor.authorLai, JLen_US
dc.contributor.authorWu, Yen_US
dc.date.accessioned2014-12-08T15:25:49Z-
dc.date.available2014-12-08T15:25:49Z-
dc.date.issued2004en_US
dc.identifier.isbn0-7803-8715-5en_US
dc.identifier.urihttp://hdl.handle.net/11536/18261-
dc.description.abstractA self-feedback ratio-memory cellular nonlinear network (SRMCNN) with the B template and the modified Hebbian learning algorithm to learn and recognize the image patterns is proposed and analyzed. In the proposed SRMCNN, the coefficients of space-variant B templates are determined from the exemplar patterns during the learning period. The weights are the ratio of the absolute summation of its neighborhood weights in the B templates was stored in the associative memory. This SRMCNN can recognize the learned patterns with distinct white-black noise and output the correct patterns. The Matlab and HSPICE software has been simulated the operation of the proposed SRMCNN. It is shown that the 18x18 SRMCNN can successfully learned and recognized 8 incompletely noisy patterns. As compared to other learnable CNN as associate memories, the proposed SRMCNN could improve pattern learning and recognition capability. The architecture can be implemented in nano-CMOS technology for giga-scale learning system in the real-time applications.en_US
dc.language.isoen_USen_US
dc.titleLearnable self-feedback ratio-memory cellular nonlinear network (SRMCNN) for associative memory applicationsen_US
dc.typeProceedings Paperen_US
dc.identifier.journalICECS 2004: 11th IEEE International Conference on Electronics, Circuits and Systemsen_US
dc.citation.spage183en_US
dc.citation.epage186en_US
dc.contributor.department電子工程學系及電子研究所zh_TW
dc.contributor.departmentDepartment of Electronics Engineering and Institute of Electronicsen_US
dc.identifier.wosnumberWOS:000228424500047-
Appears in Collections:Conferences Paper