Positivity and Stability of Cohen-Grossberg-Type Memristor Neural Networks with Unbounded Delays

Wu, A; Chen, Y; Zhu, S; Wen, S

Positivity and Stability of Cohen-Grossberg-Type Memristor Neural Networks with Unbounded Delays

Wu, A Chen, Y Zhu, S Wen, S

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Circuits and Systems Part 1: Regular Papers, 2021, 68, (11), pp. 4508-4519
Issue Date:: 2021-11-01

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Field	Value	Language
dc.contributor.author	Wu, A
dc.contributor.author	Chen, Y
dc.contributor.author	Zhu, S
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.date.accessioned	2022-04-27T06:50:06Z
dc.date.available	2022-04-27T06:50:06Z
dc.date.issued	2021-11-01
dc.identifier.citation	IEEE Transactions on Circuits and Systems Part 1: Regular Papers, 2021, 68, (11), pp. 4508-4519
dc.identifier.issn	1549-8328
dc.identifier.issn	1558-0806
dc.identifier.uri	http://hdl.handle.net/10453/156662
dc.description.abstract	This article shows a focus on the positivity and stability of Cohen-Grossberg-type time-delay memristor neural networks. We start by providing the existence and unique theorem of solutions for time-delay memristor neural networks to the case of unbounded delays. It is clear to find that the discriminate criterion of the existence and uniqueness of solutions about the argumented system with unbounded delays plays an important role in other related unbounded time-delay systems. Leveraging the Lyapunov method along with memristor nonlinearity, sufficient and necessary conditions for positivity and stability of Cohen-Grossberg-type memristor neural networks under unbounded delays are gained. Our proposed criteria don't need any strictly restrictive conditions. These theoretical results derived here will be helpful to understand the convergence performance of memristor electrical systems.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Transactions on Circuits and Systems Part 1: Regular Papers
dc.relation.isbasedon	10.1109/TCSI.2021.3113050
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.title	Positivity and Stability of Cohen-Grossberg-Type Memristor Neural Networks with Unbounded Delays
dc.type	Journal Article
utslib.citation.volume	68
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	2022-04-27T06:50:04Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	68
utslib.citation.issue	11

Abstract:

This article shows a focus on the positivity and stability of Cohen-Grossberg-type time-delay memristor neural networks. We start by providing the existence and unique theorem of solutions for time-delay memristor neural networks to the case of unbounded delays. It is clear to find that the discriminate criterion of the existence and uniqueness of solutions about the argumented system with unbounded delays plays an important role in other related unbounded time-delay systems. Leveraging the Lyapunov method along with memristor nonlinearity, sufficient and necessary conditions for positivity and stability of Cohen-Grossberg-type memristor neural networks under unbounded delays are gained. Our proposed criteria don't need any strictly restrictive conditions. These theoretical results derived here will be helpful to understand the convergence performance of memristor electrical systems.

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http://hdl.handle.net/10453/156662