Stabilization of inertial Cohen-Grossberg neural networks with generalized delays: A direct analysis approach

Han, S; Hu, C; Yu, J; Jiang, H; Wen, S

Stabilization of inertial Cohen-Grossberg neural networks with generalized delays: A direct analysis approach

Han, S Hu, C Yu, J Jiang, H Wen, S

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Chaos, Solitons and Fractals, 2021, 142, pp. 1-9
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Han, S
dc.contributor.author	Hu, C
dc.contributor.author	Yu, J
dc.contributor.author	Jiang, H
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.date.accessioned	2022-04-27T07:11:54Z
dc.date.available	2022-04-27T07:11:54Z
dc.date.issued	2021-01-01
dc.identifier.citation	Chaos, Solitons and Fractals, 2021, 142, pp. 1-9
dc.identifier.issn	0960-0779
dc.identifier.uri	http://hdl.handle.net/10453/156668
dc.description.abstract	The paper is mainly devoted to the stabilization problem of Cohen-Grossberg type inertial neural networks (INNs) with generalized delays by developing a direct analysis approach to replace the previous transformations of reduced order. Above all, a generalized form of time delays is developed to unify discrete constant delays, discrete variable delays and proportional delays. In stabilization analysis, in the absence of variable substitutions, a direct method is proposed by constructing Lyapunov functionals and designing control schemes for the addressed second-order Cohen-Grossberg INNs to achieve asymptotical or adaptive stabilization. The obtained criteria are simpler and more easily verified in applications compared with the related existing results. At last, three specified examples are provided to verify the theoretical results.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Chaos, Solitons and Fractals
dc.relation.isbasedon	10.1016/j.chaos.2020.110432
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Mathematical Physics
dc.title	Stabilization of inertial Cohen-Grossberg neural networks with generalized delays: A direct analysis approach
dc.type	Journal Article
utslib.citation.volume	142
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 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	2022-04-27T07:11:50Z
pubs.publication-status	Published
pubs.volume	142

Abstract:

The paper is mainly devoted to the stabilization problem of Cohen-Grossberg type inertial neural networks (INNs) with generalized delays by developing a direct analysis approach to replace the previous transformations of reduced order. Above all, a generalized form of time delays is developed to unify discrete constant delays, discrete variable delays and proportional delays. In stabilization analysis, in the absence of variable substitutions, a direct method is proposed by constructing Lyapunov functionals and designing control schemes for the addressed second-order Cohen-Grossberg INNs to achieve asymptotical or adaptive stabilization. The obtained criteria are simpler and more easily verified in applications compared with the related existing results. At last, three specified examples are provided to verify the theoretical results.

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http://hdl.handle.net/10453/156668