Trade off analysis between fixed-time stabilization and energy consumption of nonlinear neural networks.

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

Trade off analysis between fixed-time stabilization and energy consumption of nonlinear neural networks.

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

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Neural Netw, 2022, 148, pp. 66-73
Issue Date:: 2022-04

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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	Wang, L
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.date.accessioned	2023-02-07T02:59:45Z
dc.date.available	2022-01-07
dc.date.available	2023-02-07T02:59:45Z
dc.date.issued	2022-04
dc.identifier.citation	Neural Netw, 2022, 148, pp. 66-73
dc.identifier.issn	0893-6080
dc.identifier.issn	1879-2782
dc.identifier.uri	http://hdl.handle.net/10453/165964
dc.description.abstract	This paper concentrates on trade off analysis between fixed-time stabilization and energy consumption for a type of nonlinear neural networks (NNs). By constructing a compound switching controller and utilizing inequality techniques, a sufficient condition is proposed to ensure the fixed-time stabilization. Then, an estimate of the upper bound of the energy consumed by the controller in the control process is given. Furthermore, the quantitative analysis of the trade-off between the control time and energy consumption is studied. This article reveals that appropriate control parameters can balance the above two indicators to achieve an optimal control state. Finally, the presented theoretical results are verified by two numerical examples.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Elsevier
dc.relation.ispartof	Neural Netw
dc.relation.isbasedon	10.1016/j.neunet.2022.01.004
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Time
dc.subject.mesh	Time
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Time
dc.title	Trade off analysis between fixed-time stabilization and energy consumption of nonlinear neural networks.
dc.type	Journal Article
utslib.citation.volume	148
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	2023-02-07T02:59:42Z
pubs.publication-status	Published
pubs.volume	148

Abstract:

This paper concentrates on trade off analysis between fixed-time stabilization and energy consumption for a type of nonlinear neural networks (NNs). By constructing a compound switching controller and utilizing inequality techniques, a sufficient condition is proposed to ensure the fixed-time stabilization. Then, an estimate of the upper bound of the energy consumed by the controller in the control process is given. Furthermore, the quantitative analysis of the trade-off between the control time and energy consumption is studied. This article reveals that appropriate control parameters can balance the above two indicators to achieve an optimal control state. Finally, the presented theoretical results are verified by two numerical examples.

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http://hdl.handle.net/10453/165964