Unified analysis on multistablity of fraction-order multidimensional-valued memristive neural networks.

Wang, J; Zhu, S; Mu, C; Liu, X; Wen, S

Unified analysis on multistablity of fraction-order multidimensional-valued memristive neural networks.

Wang, J Zhu, S Mu, C Liu, X Wen, S

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Neural Netw, 2024, 179, pp. 106498
Issue Date:: 2024-11

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Field	Value	Language
dc.contributor.author	Wang, J
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	2025-01-02T06:17:54Z
dc.date.available	2024-06-26
dc.date.available	2025-01-02T06:17:54Z
dc.date.issued	2024-11
dc.identifier.citation	Neural Netw, 2024, 179, pp. 106498
dc.identifier.issn	0893-6080
dc.identifier.issn	1879-2782
dc.identifier.uri	http://hdl.handle.net/10453/182840
dc.description.abstract	This article provides a unified analysis of the multistability of fraction-order multidimensional-valued memristive neural networks (FOMVMNNs) with unbounded time-varying delays. Firstly, based on the knowledge of fractional differentiation and memristors, a unified model is established. This model is a unified form of real-valued, complex-valued, and quaternion-valued systems. Then, based on a unified method, the number of equilibrium points for FOMVMNNs is discussed. The sufficient conditions for determining the number of equilibrium points have been obtained. By using 1-norm to construct Lyapunov functions, the unified criteria for multistability of FOMVMNNs are obtained, these criteria are less conservative and easier to verify. Moreover, the attraction basins of the stable equilibrium points are estimated. Finally, two numerical simulation examples are provided to verify the correctness of the results.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Elsevier
dc.relation.ispartof	Neural Netw
dc.relation.isbasedon	10.1016/j.neunet.2024.106498
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4602 Artificial intelligence
dc.subject.classification	4611 Machine learning
dc.subject.classification	4905 Statistics
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Algorithms
dc.subject.mesh	Algorithms
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Algorithms
dc.title	Unified analysis on multistablity of fraction-order multidimensional-valued memristive neural networks.
dc.type	Journal Article
utslib.citation.volume	179
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/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Australian Artificial Intelligence Institute (AAII)
utslib.copyright.status	closed_access	*
dc.date.updated	2025-01-02T06:17:52Z
pubs.publication-status	Published
pubs.volume	179

Abstract:

This article provides a unified analysis of the multistability of fraction-order multidimensional-valued memristive neural networks (FOMVMNNs) with unbounded time-varying delays. Firstly, based on the knowledge of fractional differentiation and memristors, a unified model is established. This model is a unified form of real-valued, complex-valued, and quaternion-valued systems. Then, based on a unified method, the number of equilibrium points for FOMVMNNs is discussed. The sufficient conditions for determining the number of equilibrium points have been obtained. By using 1-norm to construct Lyapunov functions, the unified criteria for multistability of FOMVMNNs are obtained, these criteria are less conservative and easier to verify. Moreover, the attraction basins of the stable equilibrium points are estimated. Finally, two numerical simulation examples are provided to verify the correctness of the results.

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http://hdl.handle.net/10453/182840