Exponential Synchronization of Switched Neural Networks with Mixed Time-Varying Delays via Static/Dynamic Event-Triggering Rules

Cao, Y; Wang, S; Wen, S

Exponential Synchronization of Switched Neural Networks with Mixed Time-Varying Delays via Static/Dynamic Event-Triggering Rules

Cao, Y Wang, S Wen, S

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Access, 2020, 8, pp. 338-347
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Cao, Y
dc.contributor.author	Wang, S
dc.contributor.author	Wen, S https://orcid.org/0000-0002-5048-0319
dc.date.accessioned	2021-01-31T21:11:53Z
dc.date.available	2021-01-31T21:11:53Z
dc.date.issued	2020-01-01
dc.identifier.citation	IEEE Access, 2020, 8, pp. 338-347
dc.identifier.issn	2169-3536
dc.identifier.issn	2169-3536
dc.identifier.uri	http://hdl.handle.net/10453/145678
dc.description.abstract	© 2013 IEEE. This paper is devoted to the exponential synchronization of switched neural networks with mixed time-varying delays via static/dynamic event-based rules. At first, by introducing an indicator function, the switched neural networks are transformed into neural networks with general form. Then, sufficient conditions are deduced to achieve exponential synchronization for drive-response systems by two different types of event-Triggering rules, i.e., static and dynamic event-Triggering rules. Meanwhile, we can ensure that the Zeno phenomenon does not occur by proving that the time interval between two successive trigger events has a positive lower bound. Finally, two illustrative examples are elaborated to substantiate the theoretical results.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Access
dc.relation.isbasedon	10.1109/ACCESS.2019.2955939
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.title	Exponential Synchronization of Switched Neural Networks with Mixed Time-Varying Delays via Static/Dynamic Event-Triggering Rules
dc.type	Journal Article
utslib.citation.volume	8
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
dc.date.updated	2021-01-31T21:11:41Z
pubs.publication-status	Published
pubs.volume	8

Abstract:

© 2013 IEEE. This paper is devoted to the exponential synchronization of switched neural networks with mixed time-varying delays via static/dynamic event-based rules. At first, by introducing an indicator function, the switched neural networks are transformed into neural networks with general form. Then, sufficient conditions are deduced to achieve exponential synchronization for drive-response systems by two different types of event-Triggering rules, i.e., static and dynamic event-Triggering rules. Meanwhile, we can ensure that the Zeno phenomenon does not occur by proving that the time interval between two successive trigger events has a positive lower bound. Finally, two illustrative examples are elaborated to substantiate the theoretical results.

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http://hdl.handle.net/10453/145678