Stabilization of memristive neural networks with mixed time-varying delays via continuous/periodic event-based control

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

Stabilization of memristive neural networks with mixed time-varying delays via continuous/periodic event-based control

Cao, Y Wang, S Guo, Z Huang, T Wen, S

Permalink

Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Journal of the Franklin Institute, 2020, 357, (11), pp. 7122-7138
Issue Date:: 2020-07-01

Closed Access

	Filename	Description	Size
	1-s2.0-S0016003220303860-main.pdf		956.08 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Cao, Y
dc.contributor.author	Wang, S
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	2021-04-28T06:14:37Z
dc.date.available	2021-04-28T06:14:37Z
dc.date.issued	2020-07-01
dc.identifier.citation	Journal of the Franklin Institute, 2020, 357, (11), pp. 7122-7138
dc.identifier.issn	0016-0032
dc.identifier.issn	0016-0032
dc.identifier.uri	http://hdl.handle.net/10453/148465
dc.description.abstract	This paper addresses the asymptotic stabilization of memristive neural networks with mixed time-varying delays. With two different sampling schemes, sufficient conditions for asymptotic stability of the delayed memristive neural networks system can be obtained by designing appropriate event-based controllers. It is worth mentioning that the state-dependent memristive neural network model in this paper includes time-varying discrete and distributed delays, which is a generalization of the traditional neural network model. Furthermore, based on the continuous sampling event trigger control scheme, a method for designing more economical periodic sampling event trigger control scheme is proposed. Finally, to verify the validity of our conclusions, two numerical simulation examples are given.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Journal of the Franklin Institute
dc.relation.isbasedon	10.1016/j.jfranklin.2020.05.040
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0906 Electrical and Electronic Engineering
dc.subject.classification	Industrial Engineering & Automation
dc.title	Stabilization of memristive neural networks with mixed time-varying delays via continuous/periodic event-based control
dc.type	Journal Article
utslib.citation.volume	357
utslib.for	0102 Applied Mathematics
utslib.for	0906 Electrical and Electronic Engineering
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	2021-04-28T06:14:35Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	357
utslib.citation.issue	11

Abstract:

This paper addresses the asymptotic stabilization of memristive neural networks with mixed time-varying delays. With two different sampling schemes, sufficient conditions for asymptotic stability of the delayed memristive neural networks system can be obtained by designing appropriate event-based controllers. It is worth mentioning that the state-dependent memristive neural network model in this paper includes time-varying discrete and distributed delays, which is a generalization of the traditional neural network model. Furthermore, based on the continuous sampling event trigger control scheme, a method for designing more economical periodic sampling event trigger control scheme is proposed. Finally, to verify the validity of our conclusions, two numerical simulation examples are given.

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

http://hdl.handle.net/10453/148465