New Approximate Results of Fixed-Time Stabilization for Delayed Inertial Memristive Neural Networks

Zhang, G; Wen, S

New Approximate Results of Fixed-Time Stabilization for Delayed Inertial Memristive Neural Networks

Zhang, G Wen, S

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Circuits and Systems II: Express Briefs, 2024, 71, (7), pp. 3428-3432
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Zhang, G
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.date.accessioned	2024-08-07T04:51:35Z
dc.date.available	2024-08-07T04:51:35Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Transactions on Circuits and Systems II: Express Briefs, 2024, 71, (7), pp. 3428-3432
dc.identifier.issn	1549-7747
dc.identifier.issn	1558-3791
dc.identifier.uri	http://hdl.handle.net/10453/180240
dc.description.abstract	This brief discusses fixed-time stabilization of inertial memristive neural networks with mixed-time delays. First, new approximate results on settling-time of fixed-time stable are constructed, which are more accurate than the ones given in earlier works. Then, based on the new approximate results, some effective criteria are derived to achieve fixed-time stabilization with event-triggered control. Unlike reduced-order approach, this brief through nonreduced-order way to investigate such system that enhances directness of the derived results. In the end, some simulations and comparisons are listed to show the validity and advantage of the new proposed approximate settling-time and criteria of fixed-time stabilization.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Circuits and Systems II: Express Briefs
dc.relation.isbasedon	10.1109/TCSII.2024.3361670
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.title	New Approximate Results of Fixed-Time Stabilization for Delayed Inertial Memristive Neural Networks
dc.type	Journal Article
utslib.citation.volume	71
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
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Australian Artificial Intelligence Institute (AAII)
utslib.copyright.status	closed_access	*
dc.date.updated	2024-08-07T04:51:33Z
pubs.issue	7
pubs.publication-status	Published
pubs.volume	71
utslib.citation.issue	7

Abstract:

This brief discusses fixed-time stabilization of inertial memristive neural networks with mixed-time delays. First, new approximate results on settling-time of fixed-time stable are constructed, which are more accurate than the ones given in earlier works. Then, based on the new approximate results, some effective criteria are derived to achieve fixed-time stabilization with event-triggered control. Unlike reduced-order approach, this brief through nonreduced-order way to investigate such system that enhances directness of the derived results. In the end, some simulations and comparisons are listed to show the validity and advantage of the new proposed approximate settling-time and criteria of fixed-time stabilization.

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http://hdl.handle.net/10453/180240