Global synchronization of coupled delayed memristive reaction-diffusion neural networks.

Wang, S; Guo, Z; Wen, S; Huang, T

Global synchronization of coupled delayed memristive reaction-diffusion neural networks.

Wang, S Guo, Z Wen, S

Huang, T

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Neural networks : the official journal of the International Neural Network Society, 2020, 123, pp. 362-371
Issue Date:: 2020-03

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Field	Value	Language
dc.contributor.author	Wang, S
dc.contributor.author	Guo, Z
dc.contributor.author	Wen, S https://orcid.org/0000-0002-5048-0319
dc.contributor.author	Huang, T
dc.date.accessioned	2021-04-26T06:36:34Z
dc.date.available	2019-12-14
dc.date.available	2021-04-26T06:36:34Z
dc.date.issued	2020-03
dc.identifier.citation	Neural networks : the official journal of the International Neural Network Society, 2020, 123, pp. 362-371
dc.identifier.issn	0893-6080
dc.identifier.issn	1879-2782
dc.identifier.uri	http://hdl.handle.net/10453/148391
dc.description.abstract	This paper focuses on the global exponential synchronization of multiple memristive reaction-diffusion neural networks (MRDNNs) with time delay. Due to introducing the influences of space as well as time on state variables and replacing resistors with memristors in circuit realization, the state-dependent partial differential mathematical model of MRDNN is more general and realistic than traditional neural network model. Based on Lyapunov functional theory, Divergence theorem and inequality techniques, global exponential synchronization criteria of coupled delayed MRDNNs are derived via directed and undirected nonlinear coupling. Finally, three numerical simulation examples are presented to verify the feasibility of our main results.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Neural networks : the official journal of the International Neural Network Society
dc.relation.isbasedon	10.1016/j.neunet.2019.12.016
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.mesh	Models, Theoretical
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Time
dc.subject.mesh	Models, Theoretical
dc.subject.mesh	Time
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Models, Theoretical
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Time
dc.title	Global synchronization of coupled delayed memristive reaction-diffusion neural networks.
dc.type	Journal Article
utslib.citation.volume	123
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	2021-04-26T06:36:32Z
pubs.publication-status	Published
pubs.volume	123

Abstract:

This paper focuses on the global exponential synchronization of multiple memristive reaction-diffusion neural networks (MRDNNs) with time delay. Due to introducing the influences of space as well as time on state variables and replacing resistors with memristors in circuit realization, the state-dependent partial differential mathematical model of MRDNN is more general and realistic than traditional neural network model. Based on Lyapunov functional theory, Divergence theorem and inequality techniques, global exponential synchronization criteria of coupled delayed MRDNNs are derived via directed and undirected nonlinear coupling. Finally, three numerical simulation examples are presented to verify the feasibility of our main results.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/148391