Fixed/prescribed-time synchronization of BAM memristive neural networks with time-varying delays via convex analysis.

Yang, J; Chen, G; Zhu, S; Wen, S; Hu, J

Fixed/prescribed-time synchronization of BAM memristive neural networks with time-varying delays via convex analysis.

Yang, J Chen, G Zhu, S Wen, S

Hu, J

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Neural Netw, 2023, 163, pp. 53-63
Issue Date:: 2023-06

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Field	Value	Language
dc.contributor.author	Yang, J
dc.contributor.author	Chen, G
dc.contributor.author	Zhu, S
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.contributor.author	Hu, J
dc.date.accessioned	2024-01-16T21:27:10Z
dc.date.available	2023-03-21
dc.date.available	2024-01-16T21:27:10Z
dc.date.issued	2023-06
dc.identifier.citation	Neural Netw, 2023, 163, pp. 53-63
dc.identifier.issn	0893-6080
dc.identifier.issn	1879-2782
dc.identifier.uri	http://hdl.handle.net/10453/174644
dc.description.abstract	The synchronization problem of bidirectional associative memory memristive neural networks (BAMMNNs) with time-varying delays plays an essential role in the implementation and application of neural networks. Firstly, under the framework of the Filippov's solution, the discontinuous parameters of the state-dependent switching are transformed by convex analysis method, which is different from most previous approaches. Secondly, based on Lyapunov function and some inequality techniques, several conditions for the fixed-time synchronization (FXTS) of the drive-response systems are obtained by designing special control strategies. Moreover, the settling time (ST) is estimated by the improved fixed-time stability lemma. Thirdly, the driven-response BAMMNNs are investigated to be synchronized within a prescribed time by designing new controllers based on the FXTS results, where ST is irrelevant to the initial values of BAMMNNs and the parameters of controllers. Finally, a numerical simulation is exhibited to verify the correctness of the conclusions.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Neural Netw
dc.relation.isbasedon	10.1016/j.neunet.2023.03.031
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4602 Artificial intelligence
dc.subject.classification	4611 Machine learning
dc.subject.classification	4905 Statistics
dc.subject.mesh	Algorithms
dc.subject.mesh	Time Factors
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Algorithms
dc.subject.mesh	Time Factors
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Algorithms
dc.subject.mesh	Time Factors
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Computer Simulation
dc.title	Fixed/prescribed-time synchronization of BAM memristive neural networks with time-varying delays via convex analysis.
dc.type	Journal Article
utslib.citation.volume	163
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	*
dc.date.updated	2024-01-16T21:27:09Z
pubs.publication-status	Published
pubs.volume	163

Abstract:

The synchronization problem of bidirectional associative memory memristive neural networks (BAMMNNs) with time-varying delays plays an essential role in the implementation and application of neural networks. Firstly, under the framework of the Filippov's solution, the discontinuous parameters of the state-dependent switching are transformed by convex analysis method, which is different from most previous approaches. Secondly, based on Lyapunov function and some inequality techniques, several conditions for the fixed-time synchronization (FXTS) of the drive-response systems are obtained by designing special control strategies. Moreover, the settling time (ST) is estimated by the improved fixed-time stability lemma. Thirdly, the driven-response BAMMNNs are investigated to be synchronized within a prescribed time by designing new controllers based on the FXTS results, where ST is irrelevant to the initial values of BAMMNNs and the parameters of controllers. Finally, a numerical simulation is exhibited to verify the correctness of the conclusions.

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http://hdl.handle.net/10453/174644