Improved Criteria for Stability of a Class of Recurrent Neural Networks With Generalized Piecewise Constant Argument

Chen, Y; Zhu, S; Mu, C; Liu, X; Wen, S

Improved Criteria for Stability of a Class of Recurrent Neural Networks With Generalized Piecewise Constant Argument

Chen, Y Zhu, S Mu, C Liu, X Wen, S

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023, 53, (11), pp. 7246-7255
Issue Date:: 2023-11-01

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Field	Value	Language
dc.contributor.author	Chen, Y
dc.contributor.author	Zhu, S
dc.contributor.author	Mu, C
dc.contributor.author	Liu, X
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.date.accessioned	2024-01-16T21:13:19Z
dc.date.available	2024-01-16T21:13:19Z
dc.date.issued	2023-11-01
dc.identifier.citation	IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023, 53, (11), pp. 7246-7255
dc.identifier.issn	2168-2216
dc.identifier.issn	2168-2232
dc.identifier.uri	http://hdl.handle.net/10453/174621
dc.description.abstract	In this article, the global asymptotic stability of a class of recurrent neural networks (RNNs) with generalized piecewise constant argument (GPCA) is investigated. By the Banach fixed point theorem (BFPT) and comparison principle, a set of improved criteria are presented to guarantee the existence and uniqueness (EU) of the solutions and global asymptotic stability of equilibrium point for the considered RNNs. Compared with the existing results, this article not only reduces the requirements for system parameters, but also provides more criteria in different forms, which greatly improve the feasible range of the obtained criteria. The effectiveness of the obtained results are tested by some numerical examples.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Systems, Man, and Cybernetics: Systems
dc.relation.isbasedon	10.1109/TSMC.2023.3295837
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Improved Criteria for Stability of a Class of Recurrent Neural Networks With Generalized Piecewise Constant Argument
dc.type	Journal Article
utslib.citation.volume	53
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	*
dc.date.updated	2024-01-16T21:13:18Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	53
utslib.citation.issue	11

Abstract:

In this article, the global asymptotic stability of a class of recurrent neural networks (RNNs) with generalized piecewise constant argument (GPCA) is investigated. By the Banach fixed point theorem (BFPT) and comparison principle, a set of improved criteria are presented to guarantee the existence and uniqueness (EU) of the solutions and global asymptotic stability of equilibrium point for the considered RNNs. Compared with the existing results, this article not only reduces the requirements for system parameters, but also provides more criteria in different forms, which greatly improve the feasible range of the obtained criteria. The effectiveness of the obtained results are tested by some numerical examples.

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http://hdl.handle.net/10453/174621