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dc.contributor.authorWu, Chung-Yuen_US
dc.contributor.authorTsai, Su-Yungen_US
dc.date.accessioned2014-12-08T15:25:01Z-
dc.date.available2014-12-08T15:25:01Z-
dc.date.issued2006en_US
dc.identifier.isbn978-1-4244-0639-5en_US
dc.identifier.urihttp://hdl.handle.net/11536/17389-
dc.description.abstractA new type of CNN associative memory called the Autonomous ratio-memory Cellular Nonlinear Network (ARMCNN) is proposed and analyzed. In the proposed ARMCNN, the input noisy patterns am sent into the cells as the initial cell state voltages. The proposed ARMCNN has the advantages of higher recognition rate (RR), higher number of learned and recognized patterns, and smaller signal ranges of cell state voltages. The RR of the ARMCNN is also modeled as the integration of the probability functions in the convergent regions of the phase plane plot of cell state voltages. Theoretical calculation results are consistent with simulation results.en_US
dc.language.isoen_USen_US
dc.subjectcellular nonlinear network (CNN)en_US
dc.subjectratio-memory (RM)en_US
dc.titleAutonomous ratio-memory cellular nonlinear network (ARMCNN) for pattern learning and recognitionen_US
dc.typeProceedings Paperen_US
dc.identifier.journalProceedings of the 2006 10th IEEE International Workshop on Cellular Neural Networks and Their Applicationsen_US
dc.citation.spage137en_US
dc.citation.epage141en_US
dc.contributor.department電機學院zh_TW
dc.contributor.departmentCollege of Electrical and Computer Engineeringen_US
dc.identifier.wosnumberWOS:000245392200034-
Appears in Collections:Conferences Paper