Multiple asymptotical ω-periodicity of fractional-order delayed neural networks under state-dependent switching.

Ci, J; Guo, Z; Long, H; Wen, S; Huang, T

Multiple asymptotical ω-periodicity of fractional-order delayed neural networks under state-dependent switching.

Ci, J Guo, Z Long, H Wen, S

Huang, T

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Neural Netw, 2023, 157, pp. 11-25
Issue Date:: 2023-01

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Field	Value	Language
dc.contributor.author	Ci, J
dc.contributor.author	Guo, Z
dc.contributor.author	Long, H
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.contributor.author	Huang, T
dc.date.accessioned	2023-03-13T12:53:13Z
dc.date.available	2022-09-29
dc.date.available	2023-03-13T12:53:13Z
dc.date.issued	2023-01
dc.identifier.citation	Neural Netw, 2023, 157, pp. 11-25
dc.identifier.issn	0893-6080
dc.identifier.issn	1879-2782
dc.identifier.uri	http://hdl.handle.net/10453/167169
dc.description.abstract	This paper presents theoretical results on multiple asymptotical ω-periodicity of a state-dependent switching fractional-order neural network with time delays and sigmoidal activation functions. Firstly, by combining the geometrical properties of activation functions with the range of switching threshold, a partition of state space is given. Then, the conditions guaranteeing that the solutions can approach each other infinitely in each positive invariant set are derived. Furthermore, the S-asymptotical ω-periodicity and the convergence of solutions in positive invariant sets are discussed. It is worth noting that the number of attractors increases to 3n from 2n in a neural network without switching. Finally, three numerical examples are given to substantiate the theoretical results.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Elsevier
dc.relation.ispartof	Neural Netw
dc.relation.isbasedon	10.1016/j.neunet.2022.09.034
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.mesh	Algorithms
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Periodicity
dc.subject.mesh	Periodicity
dc.subject.mesh	Algorithms
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Algorithms
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Periodicity
dc.title	Multiple asymptotical ω-periodicity of fractional-order delayed neural networks under state-dependent switching.
dc.type	Journal Article
utslib.citation.volume	157
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	2023-03-13T12:53:11Z
pubs.publication-status	Published
pubs.volume	157

Abstract:

This paper presents theoretical results on multiple asymptotical ω-periodicity of a state-dependent switching fractional-order neural network with time delays and sigmoidal activation functions. Firstly, by combining the geometrical properties of activation functions with the range of switching threshold, a partition of state space is given. Then, the conditions guaranteeing that the solutions can approach each other infinitely in each positive invariant set are derived. Furthermore, the S-asymptotical ω-periodicity and the convergence of solutions in positive invariant sets are discussed. It is worth noting that the number of attractors increases to 3n from 2n in a neural network without switching. Finally, three numerical examples are given to substantiate the theoretical results.

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http://hdl.handle.net/10453/167169