Fuzzy adaptive synchronization of time-reversed chaotic systems via a new adaptive control strategy

Li, SY; Yang, CH; Chen, SA; Ko, LW; Lin, CT

Fuzzy adaptive synchronization of time-reversed chaotic systems via a new adaptive control strategy

Li, SY Yang, CH Chen, SA Ko, LW Lin, CT

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Publication Type:: Journal Article
Citation:: Information Sciences, 2013, 222 pp. 486 - 500
Issue Date:: 2013-02-10

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Field	Value	Language
dc.contributor.author	Li, SY	en_US
dc.contributor.author	Yang, CH	en_US
dc.contributor.author	Chen, SA	en_US
dc.contributor.author	Ko, LW	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.issued	2013-02-10	en_US
dc.identifier.citation	Information Sciences, 2013, 222 pp. 486 - 500	en_US
dc.identifier.issn	0020-0255	en_US
dc.identifier.uri	http://hdl.handle.net/10453/116977
dc.description.abstract	A novel adaptive control strategy is proposed herein to increase the efficiency of adaptive control by combining Takagi-Sugeno (T-S) fuzzy modeling and the Ge-Yao-Chen (GYC) partial region stability theory. This approach provides two major contributions: (1) increased synchronization efficiency, especially for parameters tracing and (2) a simpler controller design. Two simulated cases are presented for comparison: Case 1 utilizes normal adaptive synchronization, whereas Case 2 utilizes the Takagi-Sugeno (T-S) fuzzy model-based Lorenz systems to realize adaptive synchronization via the new adaptive scheme. The simulation results demonstrate the effectiveness and feasibility of our new adaptive strategy. © 2012 Elsevier Inc. All rights reserved.	en_US
dc.relation.ispartof	Information Sciences	en_US
dc.relation.isbasedon	10.1016/j.ins.2012.08.007	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Fuzzy adaptive synchronization of time-reversed chaotic systems via a new adaptive control strategy	en_US
dc.type	Journal Article
utslib.citation.volume	222	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	222	en_US

Abstract:

A novel adaptive control strategy is proposed herein to increase the efficiency of adaptive control by combining Takagi-Sugeno (T-S) fuzzy modeling and the Ge-Yao-Chen (GYC) partial region stability theory. This approach provides two major contributions: (1) increased synchronization efficiency, especially for parameters tracing and (2) a simpler controller design. Two simulated cases are presented for comparison: Case 1 utilizes normal adaptive synchronization, whereas Case 2 utilizes the Takagi-Sugeno (T-S) fuzzy model-based Lorenz systems to realize adaptive synchronization via the new adaptive scheme. The simulation results demonstrate the effectiveness and feasibility of our new adaptive strategy. © 2012 Elsevier Inc. All rights reserved.

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http://hdl.handle.net/10453/116977