Global exponential synchronization of two memristor-based recurrent neural networks with time delays via static or dynamic coupling

Guo, Z; Wang, J; Yan, Z

Global exponential synchronization of two memristor-based recurrent neural networks with time delays via static or dynamic coupling

Guo, Z Wang, J Yan, Z

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2015, 45 (2), pp. 235 - 249
Issue Date:: 2015-02-01

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Field	Value	Language
dc.contributor.author	Guo, Z	en_US
dc.contributor.author	Wang, J	en_US
dc.contributor.author	Yan, Z https://orcid.org/0000-0003-3368-2100	en_US
dc.date.issued	2015-02-01	en_US
dc.identifier.citation	IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2015, 45 (2), pp. 235 - 249	en_US
dc.identifier.issn	2168-2216	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119472
dc.description.abstract	© 2013 IEEE. This paper is concerned with the global exponential synchronization of two memristor-based recurrent neural networks (MRNNs) with time delays via static or dynamic coupling. First, four coupling rules (i.e., static state coupling, static output coupling, dynamic state coupling, and dynamic output coupling) are designed for the exponential synchronization of drive-response pair of MRNNs. Then, several global exponential synchronization criteria are derived by constructing suitable Lyapunov-Krasovskii functionals based on the Lyapunov stability theory. Compared with existing results on synchronization of MRNNs, the conditions herein are easy to be verified. Moreover, the designed dynamic state coupling and output coupling rules have good anti-interference capacity. Finally, two illustrative examples are presented to substantiate the effectiveness and characteristics of the presented theoretical results.	en_US
dc.relation.ispartof	IEEE Transactions on Systems, Man, and Cybernetics: Systems	en_US
dc.relation.isbasedon	10.1109/TSMC.2014.2343911	en_US
dc.title	Global exponential synchronization of two memristor-based recurrent neural networks with time delays via static or dynamic coupling	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	45	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	0906 Electrical and Electronic 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/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	45	en_US

Abstract:

© 2013 IEEE. This paper is concerned with the global exponential synchronization of two memristor-based recurrent neural networks (MRNNs) with time delays via static or dynamic coupling. First, four coupling rules (i.e., static state coupling, static output coupling, dynamic state coupling, and dynamic output coupling) are designed for the exponential synchronization of drive-response pair of MRNNs. Then, several global exponential synchronization criteria are derived by constructing suitable Lyapunov-Krasovskii functionals based on the Lyapunov stability theory. Compared with existing results on synchronization of MRNNs, the conditions herein are easy to be verified. Moreover, the designed dynamic state coupling and output coupling rules have good anti-interference capacity. Finally, two illustrative examples are presented to substantiate the effectiveness and characteristics of the presented theoretical results.

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http://hdl.handle.net/10453/119472