Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/5270
DC Field | Value | Language |
---|---|---|
dc.contributor | Institute of Textiles and Clothing | - |
dc.creator | Tang, Y | - |
dc.creator | Wang, Z | - |
dc.creator | Wong, WKC | - |
dc.creator | Kurths, J | - |
dc.creator | Fang, JA | - |
dc.date.accessioned | 2014-12-11T08:29:02Z | - |
dc.date.available | 2014-12-11T08:29:02Z | - |
dc.identifier.issn | 1054-1500 | - |
dc.identifier.uri | http://hdl.handle.net/10397/5270 | - |
dc.language.iso | en | en_US |
dc.publisher | American Institute of Physics | en_US |
dc.rights | © 2011 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. The following article appeared in Y. Tang et al., Chaos: an interdisciplinary journal of nonlinear science 21, 025114 (2011) and may be found at http://link.aip.org/link/?cha/21/025114 | en_US |
dc.subject | Cellular biophysics | en_US |
dc.subject | Chaos | en_US |
dc.subject | Complex networks | en_US |
dc.subject | Learning (artificial intelligence) | en_US |
dc.subject | Neural nets | en_US |
dc.subject | Sychronisation | en_US |
dc.title | Multiobjective synchronization of coupled systems | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.description.otherinformation | Author name used in this publication: W. K. Wong | en_US |
dc.identifier.spage | 1 | - |
dc.identifier.epage | 12 | - |
dc.identifier.volume | 21 | - |
dc.identifier.issue | 2 | - |
dc.identifier.doi | 10.1063/1.3595701 | - |
dcterms.abstract | In this paper, multiobjective synchronization of chaotic systems is investigated by especially simultaneously minimizing optimization of control cost and convergence speed. The coupling form and coupling strength are optimized by an improved multiobjective evolutionary approach that includes a hybrid chromosome representation. The hybrid encoding scheme combines binary representation with real number representation. The constraints on the coupling form are also considered by converting the multiobjective synchronization into a multiobjective constraint problem. In addition, the performances of the adaptive learning method and non-dominated sorting genetic algorithm-II as well as the effectiveness and contributions of the proposed approach are analyzed and validated through the Rössler system in a chaotic or hyperchaotic regime and delayed chaotic neural networks. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Chaos, June 2011, v. 21, no. 2, 025114, p. 1-12 | - |
dcterms.isPartOf | Chaos | - |
dcterms.issued | 2011-06 | - |
dc.identifier.isi | WOS:000292330300050 | - |
dc.identifier.scopus | 2-s2.0-79959974477 | - |
dc.identifier.eissn | 1089-7682 | - |
dc.identifier.rosgroupid | r55727 | - |
dc.description.ros | 2010-2011 > Academic research: refereed > Publication in refereed journal | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | VoR allowed | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Tang_Multiobjective_Synchronization_Coupled.pdf | 3.1 MB | Adobe PDF | View/Open |
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