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dc.contributor.authorChen, SSen_US
dc.contributor.authorShih, CWen_US
dc.date.accessioned2014-12-08T15:38:46Z-
dc.date.available2014-12-08T15:38:46Z-
dc.date.issued2004-08-01en_US
dc.identifier.issn0218-1274en_US
dc.identifier.urihttp://dx.doi.org/10.1142/S0218127404011053en_US
dc.identifier.urihttp://hdl.handle.net/11536/26533-
dc.description.abstractThis presentation investigates the dynamics of discrete-time cellular neural networks (DT-CNN). In contrast to classical neural networks that are mostly gradient-like systems, DT-CNN possesses both complete stability and chaotic behaviours as different parameters are considered. An energy-like function which decreases along orbits of DT-CNN as well as the existence of a globally attracting set are derived. Complete stability can then be concluded, with further analysis on the sets on which the energy function is constant. The formations of saturated stationary patterns for DT-CNN are shown to be analogous to the ones in continuous-time CNN. Thus, DT-CNN shares similar properties with continuous-time CNN. By confirming the existence of snap-back repellers, hence transversal homoclinic orbits, we also conclude that DT-CNN with certain parameters exhibits chaotic dynamics, according to the theorem by Marotto.en_US
dc.language.isoen_USen_US
dc.subjectcellular neural networken_US
dc.subjectpattern formationen_US
dc.subjectcomplete stabilityen_US
dc.subjecthomoclinic orbitsen_US
dc.subjectsnap-back repelleren_US
dc.subjectchaosen_US
dc.titleDynamics for discrete-time cellular neural networksen_US
dc.typeArticleen_US
dc.identifier.doi10.1142/S0218127404011053en_US
dc.identifier.journalINTERNATIONAL JOURNAL OF BIFURCATION AND CHAOSen_US
dc.citation.volume14en_US
dc.citation.issue8en_US
dc.citation.spage2667en_US
dc.citation.epage2687en_US
dc.contributor.department應用數學系zh_TW
dc.contributor.departmentDepartment of Applied Mathematicsen_US
dc.identifier.wosnumberWOS:000224603300005-
dc.citation.woscount9-
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