Multistability and Robustness of Competitive Neural Networks With Time-Varying Delays.

Zhu, S; Zhang, J; Liu, X; Shen, M; Wen, S; Mu, C

Multistability and Robustness of Competitive Neural Networks With Time-Varying Delays.

Zhu, S Zhang, J Liu, X Shen, M Wen, S

Mu, C

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Trans Neural Netw Learn Syst, 2023, PP, (99)
Issue Date:: 2023-10-11

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Field	Value	Language
dc.contributor.author	Zhu, S
dc.contributor.author	Zhang, J
dc.contributor.author	Liu, X
dc.contributor.author	Shen, M
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.contributor.author	Mu, C
dc.date.accessioned	2024-01-16T21:15:46Z
dc.date.available	2024-01-16T21:15:46Z
dc.date.issued	2023-10-11
dc.identifier.citation	IEEE Trans Neural Netw Learn Syst, 2023, PP, (99)
dc.identifier.issn	2162-237X
dc.identifier.issn	2162-2388
dc.identifier.uri	http://hdl.handle.net/10453/174626
dc.description.abstract	This article is devoted to analyzing the multistability and robustness of competitive neural networks (NNs) with time-varying delays. Based on the geometrical structure of activation functions, some sufficient conditions are proposed to ascertain the coexistence of ∏i=1n(2Ri+1) equilibrium points, ∏i=1n(Ri+1) of them are locally exponentially stable, where n represents a dimension of system and Ri is the parameter related to activation functions. The derived stability results not only involve exponential stability but also include power stability and logarithmical stability. In addition, the robustness of ∏i=1n(Ri+1) stable equilibrium points is discussed in the presence of perturbations. Compared with previous papers, the conclusions proposed in this article are easy to verify and enrich the existing stability theories of competitive NNs. Finally, numerical examples are provided to support theoretical results.
dc.language	eng
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Trans Neural Netw Learn Syst
dc.relation.isbasedon	10.1109/TNNLS.2023.3321434
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4602 Artificial intelligence
dc.title	Multistability and Robustness of Competitive Neural Networks With Time-Varying Delays.
dc.type	Journal Article
utslib.citation.volume	PP
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	2024-01-16T21:15:44Z
pubs.issue	99
pubs.publication-status	Published online
pubs.volume	PP
utslib.citation.issue	99

Abstract:

This article is devoted to analyzing the multistability and robustness of competitive neural networks (NNs) with time-varying delays. Based on the geometrical structure of activation functions, some sufficient conditions are proposed to ascertain the coexistence of ∏i=1n(2Ri+1) equilibrium points, ∏i=1n(Ri+1) of them are locally exponentially stable, where n represents a dimension of system and Ri is the parameter related to activation functions. The derived stability results not only involve exponential stability but also include power stability and logarithmical stability. In addition, the robustness of ∏i=1n(Ri+1) stable equilibrium points is discussed in the presence of perturbations. Compared with previous papers, the conclusions proposed in this article are easy to verify and enrich the existing stability theories of competitive NNs. Finally, numerical examples are provided to support theoretical results.

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http://hdl.handle.net/10453/174626