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dc.contributor.authorLi, Shih-Yuen_US
dc.contributor.authorChen, Hsien-Kengen_US
dc.contributor.authorTam, Lap-Mouen_US
dc.contributor.authorHuang, Sheng-Chiehen_US
dc.contributor.authorGe, Zheng-Mingen_US
dc.date.accessioned2014-12-08T15:36:16Z-
dc.date.available2014-12-08T15:36:16Z-
dc.date.issued2014-09-01en_US
dc.identifier.issn0020-0255en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.ins.2014.02.128en_US
dc.identifier.urihttp://hdl.handle.net/11536/24601-
dc.description.abstractIn this paper, (1) a new fuzzy model is presented to simulate two different chaotic systems with different numbers of nonlinear terms and (2) a new adaptive approach and a new control Lyapunov function are proposed to synchronize these two different fuzzy chaotic systems and speed up the convergence of errors. By using this new model, the numbers of fuzzy rules of chaotic systems can be reduced from 2(N) to 2 x N and only 2 subsystems are needed, where N is the number of nonlinear terms. The fuzzy systems become much simpler. In addition, through the new fuzzy model, the new fuzzy systems are much simpler than T-S fuzzy systems (when nonlinear systems are complicated) and can be used to any other kind of application in fuzzy logic control or fuzzy modeling. Mathieu-Van der Pol system (which is called M-V system in this paper) and Quantum cellular neural networks nanosystem (which is called Q-CNN system in this paper) are used for illustrations in numerical simulation results to show the effectiveness and feasibility of our new adaptive approach and new control Lyapunov function. The T-S fuzzy modeling and traditional adaptive control are also given in Appendices B and C for comparison. (C) 2014 Elsevier Inc. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectNew fuzzy modelen_US
dc.subjectNew control Lyapunov functionen_US
dc.subjectNew pragmatical adaptive methoden_US
dc.subjectMathieu-Van der Pol systemen_US
dc.titlePragmatical adaptive synchronization - New fuzzy model of two different and complex chaotic systems by new adaptive controlen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.ins.2014.02.128en_US
dc.identifier.journalINFORMATION SCIENCESen_US
dc.citation.volume277en_US
dc.citation.issueen_US
dc.citation.spage458en_US
dc.citation.epage480en_US
dc.contributor.department機械工程學系zh_TW
dc.contributor.department電機工程學系zh_TW
dc.contributor.department腦科學研究中心zh_TW
dc.contributor.departmentDepartment of Mechanical Engineeringen_US
dc.contributor.departmentDepartment of Electrical and Computer Engineeringen_US
dc.contributor.departmentBrain Research Centeren_US
dc.identifier.wosnumberWOS:000338390200030-
dc.citation.woscount0-
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