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dc.contributor.authorWang, Chi-Hsuen_US
dc.contributor.authorChen, Chun-Yaoen_US
dc.date.accessioned2015-12-02T02:59:12Z-
dc.date.available2015-12-02T02:59:12Z-
dc.date.issued2015-06-01en_US
dc.identifier.issn1562-2479en_US
dc.identifier.urihttp://dx.doi.org/10.1007/s40815-015-0030-7en_US
dc.identifier.urihttp://hdl.handle.net/11536/127909-
dc.description.abstractIn this paper, a new mean-based adaptive fuzzy neural network sliding mode control is developed to perform the chaos synchronization among the master-slave fractional order uncertain systems. The mean-based expansion is adopted to replace the traditional Taylor expansion to transform a nonlinear function into a partially linear form for the linearization of nonlinear systems. In comparison with the traditional Taylor method, the proposed mean-based method can estimate the first-order derivative term on the identifier model, which will somehow alleviate the computational burden. Based on the learning algorithms, the adaptive laws and control laws can be tuned on-line to synchronize the master-slave fractional order uncertain systems. Furthermore, the stability of the closed-loop system can not only be assured but the synchronization deviation of external perturbation can also be alleviated. Finally, simulation examples are illustrated to demonstrate the feasibility and the synchronization performance of this new approach.en_US
dc.language.isoen_USen_US
dc.subjectAdaptive fuzzy neural networken_US
dc.subjectSliding mode controlen_US
dc.subjectFractional orderen_US
dc.subjectChaotic systemsen_US
dc.subjectMean-based expansionen_US
dc.titleIntelligent Chaos Synchronization of Fractional Order Systems via Mean-Based Slide Mode Controlleren_US
dc.typeArticleen_US
dc.identifier.doi10.1007/s40815-015-0030-7en_US
dc.identifier.journalINTERNATIONAL JOURNAL OF FUZZY SYSTEMSen_US
dc.citation.volume17en_US
dc.citation.spage144en_US
dc.citation.epage157en_US
dc.contributor.department電機工程學系zh_TW
dc.contributor.departmentDepartment of Electrical and Computer Engineeringen_US
dc.identifier.wosnumberWOS:000356335400004en_US
dc.citation.woscount0en_US
Appears in Collections:Articles