Reachable Set Estimation for Delayed Memristive Neural Networks With Bounded Disturbances

Zhu, S; Guo, M; Mu, C; Liu, X; Wen, S

Reachable Set Estimation for Delayed Memristive Neural Networks With Bounded Disturbances

Zhu, S Guo, M Mu, C Liu, X Wen, S

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2024, 54, (9), pp. 5523-5528
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Zhu, S
dc.contributor.author	Guo, M
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	2024-09-05T10:50:53Z
dc.date.available	2024-09-05T10:50:53Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2024, 54, (9), pp. 5523-5528
dc.identifier.issn	2168-2216
dc.identifier.issn	2168-2232
dc.identifier.uri	http://hdl.handle.net/10453/180684
dc.description.abstract	This brief discusses the reachable set estimation (RSE) problem for memristive neural networks (MNNs) involving time-varying delays and bounded disturbances. The reachable sets of the considered MNNs under zero and nonzero initial conditions are estimated by two novel algebraic criteria, respectively. Compared with the existing results, the conclusions are easy to verify, and the obtained reachable sets are more accurate. Finally, the validity of the theoretical results is illustrated by two examples.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Systems, Man, and Cybernetics: Systems
dc.relation.isbasedon	10.1109/TSMC.2024.3407793
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Reachable Set Estimation for Delayed Memristive Neural Networks With Bounded Disturbances
dc.type	Journal Article
utslib.citation.volume	54
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	2024-09-05T10:50:51Z
pubs.issue	9
pubs.publication-status	Published
pubs.volume	54
utslib.citation.issue	9

Abstract:

This brief discusses the reachable set estimation (RSE) problem for memristive neural networks (MNNs) involving time-varying delays and bounded disturbances. The reachable sets of the considered MNNs under zero and nonzero initial conditions are estimated by two novel algebraic criteria, respectively. Compared with the existing results, the conclusions are easy to verify, and the obtained reachable sets are more accurate. Finally, the validity of the theoretical results is illustrated by two examples.

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http://hdl.handle.net/10453/180684