Stability Analysis of Autonomous Ratio-Memory Cellular Nonlinear Networks for Pattern Recognition
| dc.citation.epage | 2167 | en_US |
| dc.citation.issue | 8 | en_US |
| dc.citation.spage | 2156 | en_US |
| dc.citation.volume | 57 | en_US |
| dc.citation.woscount | 1 | |
| dc.contributor.author | Tsai, Su-Yung | en_US |
| dc.contributor.author | Wang, Chi-Hsu | en_US |
| dc.contributor.author | Wu, Chung-Yu | en_US |
| dc.contributor.department | 電子工程學系及電子研究所 | zh_TW |
| dc.contributor.department | Department of Electronics Engineering and Institute of Electronics | en_US |
| dc.date.accessioned | 2014-12-08T15:48:37Z | |
| dc.date.available | 2014-12-08T15:48:37Z | |
| dc.date.issued | 2010-08-01 | en_US |
| dc.description.abstract | The stability analysis via the Lyapunov theorem for Autonomous Ratio-Memory Cellular Nonlinear Networks (ARMCNNs) is proposed. A conservative domain of attraction (DOA) is found from the stability analysis through a graphical method without complicated numerical analysis. The stability analysis shows that ARMCNNs can tolerate large ratio weight variations. This paper also presents the ARMCNN with self-feedback (SARMCNN) to overcome the problem of isolated neurons due to low correlation between neighboring neurons. The SARMCNN recognition rate (RR) is compared with other CNN constructed via the singular value decomposition technique (SVD-CNN). | en_US |
| dc.identifier.doi | 10.1109/TCSI.2009.2037450 | en_US |
| dc.identifier.issn | 1549-8328 | en_US |
| dc.identifier.journal | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS | en_US |
| dc.identifier.uri | http://dx.doi.org/10.1109/TCSI.2009.2037450 | en_US |
| dc.identifier.uri | https://ir.lib.nycu.edu.tw/handle/11536/32333 | |
| dc.identifier.wosnumber | WOS:000282987200031 | |
| dc.language.iso | en_US | en_US |
| dc.subject | Cellular nonlinear network (CNN) | en_US |
| dc.subject | domain of attraction (DOA) | en_US |
| dc.subject | Lyapunov stability | en_US |
| dc.subject | ratio memory (RM) | en_US |
| dc.subject | Hebbian learning rule | en_US |
| dc.title | Stability Analysis of Autonomous Ratio-Memory Cellular Nonlinear Networks for Pattern Recognition | en_US |
| dc.type | Article | en_US |