Cluster Synchronization of Multiple Fractional-Order Recurrent Neural Networks With Time-Varying Delays.

Liu, P; Xu, M; Sun, J; Wen, S

Cluster Synchronization of Multiple Fractional-Order Recurrent Neural Networks With Time-Varying Delays.

Liu, P Xu, M Sun, J Wen, S

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Neural Networks and Learning Systems, 2022, PP, (99), pp. 1-12
Issue Date:: 2022-10-29

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Field	Value	Language
dc.contributor.author	Liu, P
dc.contributor.author	Xu, M
dc.contributor.author	Sun, J
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.date.accessioned	2023-02-28T03:25:22Z
dc.date.available	2023-02-28T03:25:22Z
dc.date.issued	2022-10-29
dc.identifier.citation	IEEE Transactions on Neural Networks and Learning Systems, 2022, PP, (99), pp. 1-12
dc.identifier.issn	1045-9227
dc.identifier.issn	2162-2388
dc.identifier.uri	http://hdl.handle.net/10453/166512
dc.description.abstract	This article focuses on the cluster synchronization of multiple fractional-order recurrent neural networks (FNNs) with time-varying delays. Sufficient criteria are deduced for realizing cluster synchronization of multiple FNNs via a pinning control by applying an extended Halanay inequality applicable for time-delayed fractional-order differential equations. Moreover, an adaptive control applicable for the synchronization of fractional-order systems with time-varying delays is proposed, under which sufficient criteria are derived for realizing cluster synchronization of multiple FNNs with time-varying delays. Finally, two examples are presented to illustrate the effectiveness of the theoretical results.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Transactions on Neural Networks and Learning Systems
dc.relation.isbasedon	10.1109/TNNLS.2021.3121516
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Cluster Synchronization of Multiple Fractional-Order Recurrent Neural Networks With Time-Varying Delays.
dc.type	Journal Article
utslib.citation.volume	PP
utslib.location.activity	United States
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/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-02-28T03:25:21Z
pubs.issue	99
pubs.publication-status	Published online
pubs.volume	PP
utslib.citation.issue	99

Abstract:

This article focuses on the cluster synchronization of multiple fractional-order recurrent neural networks (FNNs) with time-varying delays. Sufficient criteria are deduced for realizing cluster synchronization of multiple FNNs via a pinning control by applying an extended Halanay inequality applicable for time-delayed fractional-order differential equations. Moreover, an adaptive control applicable for the synchronization of fractional-order systems with time-varying delays is proposed, under which sufficient criteria are derived for realizing cluster synchronization of multiple FNNs with time-varying delays. Finally, two examples are presented to illustrate the effectiveness of the theoretical results.

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http://hdl.handle.net/10453/166512