Global exponential anti-synchronization for delayed memristive neural networks via event-triggering method

Ni, X; Cao, Y; Guo, Z; Huang, T; Wen, S

Global exponential anti-synchronization for delayed memristive neural networks via event-triggering method

Ni, X Cao, Y Guo, Z Huang, T Wen, S

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Publisher:: SPRINGER LONDON LTD
Publication Type:: Journal Article
Citation:: Neural Computing and Applications, 2020, 32, (17), pp. 13521-13535
Issue Date:: 2020-09-01

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Field	Value	Language
dc.contributor.author	Ni, X
dc.contributor.author	Cao, Y
dc.contributor.author	Guo, Z
dc.contributor.author	Huang, T
dc.contributor.author	Wen, S https://orcid.org/0000-0002-5048-0319
dc.date.accessioned	2021-04-26T06:33:28Z
dc.date.available	2021-04-26T06:33:28Z
dc.date.issued	2020-09-01
dc.identifier.citation	Neural Computing and Applications, 2020, 32, (17), pp. 13521-13535
dc.identifier.issn	0941-0643
dc.identifier.issn	1433-3058
dc.identifier.uri	http://hdl.handle.net/10453/148390
dc.description.abstract	This paper studies the exponential anti-synchronization problem of memristive delayed neural networks under the event-triggered controller.To reduce the recalculation of the control signals, two event-triggered control strategies including static and dynamic are proposed. A novel Lyapunov function is constructed to analyze the global exponential anti-synchronization problem. By analysis, we can choose the suitable parameter of the controller to realize global exponential anti-synchronization with a given convergence rate γ without wasting a lot of control resources. Moreover, under event-triggering conditions given in our theorem, we derive that the Zeno behavior will not happen. Finally, numerical examples are given to validate our theorem.
dc.language	English
dc.publisher	SPRINGER LONDON LTD
dc.relation	National Natural Science Foundation of China61673187
dc.relation.ispartof	Neural Computing and Applications
dc.relation.isbasedon	10.1007/s00521-020-04762-5
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Global exponential anti-synchronization for delayed memristive neural networks via event-triggering method
dc.type	Journal Article
utslib.citation.volume	32
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1702 Cognitive Sciences
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	2021-04-26T06:33:26Z
pubs.issue	17
pubs.publication-status	Published
pubs.volume	32
utslib.citation.issue	17

Abstract:

This paper studies the exponential anti-synchronization problem of memristive delayed neural networks under the event-triggered controller.To reduce the recalculation of the control signals, two event-triggered control strategies including static and dynamic are proposed. A novel Lyapunov function is constructed to analyze the global exponential anti-synchronization problem. By analysis, we can choose the suitable parameter of the controller to realize global exponential anti-synchronization with a given convergence rate γ without wasting a lot of control resources. Moreover, under event-triggering conditions given in our theorem, we derive that the Zeno behavior will not happen. Finally, numerical examples are given to validate our theorem.

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http://hdl.handle.net/10453/148390