Comprehensive analysis of fixed-time stability and energy cost for delay neural networks.

Wang, Y; Zhu, S; Shao, H; Feng, Y; Wang, L; Wen, S

Comprehensive analysis of fixed-time stability and energy cost for delay neural networks.

Wang, Y Zhu, S Shao, H Feng, Y Wang, L Wen, S

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Neural Netw, 2022, 155, pp. 413-421
Issue Date:: 2022-11

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Field	Value	Language
dc.contributor.author	Wang, Y
dc.contributor.author	Zhu, S
dc.contributor.author	Shao, H
dc.contributor.author	Feng, Y
dc.contributor.author	Wang, L
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.date.accessioned	2023-02-28T03:22:34Z
dc.date.available	2022-08-25
dc.date.available	2023-02-28T03:22:34Z
dc.date.issued	2022-11
dc.identifier.citation	Neural Netw, 2022, 155, pp. 413-421
dc.identifier.issn	0893-6080
dc.identifier.issn	1879-2782
dc.identifier.uri	http://hdl.handle.net/10453/166509
dc.description.abstract	This paper focuses on comprehensive analysis of fixed-time stability and energy consumed by controller in nonlinear neural networks with time-varying delays. A sufficient condition is provided to assure fixed-time stability by developing a global composite switched controller and employing inequality techniques. Then the specific expression of the upper of energy required for achieving control is deduced. Moreover, the comprehensive analysis of the energy cost and fixed-time stability is investigated utilizing a dual-objective optimization function. It illustrates that adjusting the control parameters can make the system converge to the equilibrium point under better control state. Finally, one numerical example is presented to verify the effectiveness of the provided control scheme.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Elsevier
dc.relation.ispartof	Neural Netw
dc.relation.isbasedon	10.1016/j.neunet.2022.08.024
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.mesh	Time Factors
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Algorithms
dc.subject.mesh	Algorithms
dc.subject.mesh	Time Factors
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Time Factors
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Algorithms
dc.title	Comprehensive analysis of fixed-time stability and energy cost for delay neural networks.
dc.type	Journal Article
utslib.citation.volume	155
utslib.location.activity	United States
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	*
dc.date.updated	2023-02-28T03:22:33Z
pubs.publication-status	Published
pubs.volume	155

Abstract:

This paper focuses on comprehensive analysis of fixed-time stability and energy consumed by controller in nonlinear neural networks with time-varying delays. A sufficient condition is provided to assure fixed-time stability by developing a global composite switched controller and employing inequality techniques. Then the specific expression of the upper of energy required for achieving control is deduced. Moreover, the comprehensive analysis of the energy cost and fixed-time stability is investigated utilizing a dual-objective optimization function. It illustrates that adjusting the control parameters can make the system converge to the equilibrium point under better control state. Finally, one numerical example is presented to verify the effectiveness of the provided control scheme.

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http://hdl.handle.net/10453/166509