Synchronization of discrete-time recurrent neural networks with time-varying delays via quantized sliding mode control

Sun, B; Cao, Y; Guo, Z; Yan, Z; Wen, S

Synchronization of discrete-time recurrent neural networks with time-varying delays via quantized sliding mode control

Sun, B Cao, Y Guo, Z Yan, Z

Wen, S

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Publisher:: ELSEVIER SCIENCE INC
Publication Type:: Journal Article
Citation:: Applied Mathematics and Computation, 2020, 375
Issue Date:: 2020-06-15

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Field	Value	Language
dc.contributor.author	Sun, B
dc.contributor.author	Cao, Y
dc.contributor.author	Guo, Z
dc.contributor.author	Yan, Z https://orcid.org/0000-0003-3368-2100
dc.contributor.author	Wen, S https://orcid.org/0000-0002-5048-0319
dc.date.accessioned	2021-04-28T06:21:46Z
dc.date.available	2021-04-28T06:21:46Z
dc.date.issued	2020-06-15
dc.identifier.citation	Applied Mathematics and Computation, 2020, 375
dc.identifier.issn	0096-3003
dc.identifier.issn	1873-5649
dc.identifier.uri	http://hdl.handle.net/10453/148467
dc.description.abstract	In this paper, we discuss synchronization of discrete-time recurrent neural networks (DRNNs) with time-varying delays via quantized sliding mode control. A feedback controller based on sliding mode control is firstly imported in the synchronization of DRNNs. The activation functional in our paper can be more relaxed than the other papers which should satisfy the Lipschitz conditions. For the sake of reducing the computational complexity and conservatism, we consider two quantized methods with uniform and logarithmic quantizer. We gain some specific conditions to ensure the synchronization of discrete-time system. Several examples are presented to support our theorem in the ending.
dc.language	English
dc.publisher	ELSEVIER SCIENCE INC
dc.relation.ispartof	Applied Mathematics and Computation
dc.relation.isbasedon	10.1016/j.amc.2020.125093
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0103 Numerical and Computational Mathematics, 0802 Computation Theory and Mathematics
dc.subject.classification	Numerical & Computational Mathematics
dc.title	Synchronization of discrete-time recurrent neural networks with time-varying delays via quantized sliding mode control
dc.type	Journal Article
utslib.citation.volume	375
utslib.for	0102 Applied Mathematics
utslib.for	0103 Numerical and Computational Mathematics
utslib.for	0802 Computation Theory and Mathematics
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	*
pubs.consider-herdc	false
dc.date.updated	2021-04-28T06:21:44Z
pubs.publication-status	Published
pubs.volume	375

Abstract:

In this paper, we discuss synchronization of discrete-time recurrent neural networks (DRNNs) with time-varying delays via quantized sliding mode control. A feedback controller based on sliding mode control is firstly imported in the synchronization of DRNNs. The activation functional in our paper can be more relaxed than the other papers which should satisfy the Lipschitz conditions. For the sake of reducing the computational complexity and conservatism, we consider two quantized methods with uniform and logarithmic quantizer. We gain some specific conditions to ensure the synchronization of discrete-time system. Several examples are presented to support our theorem in the ending.

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http://hdl.handle.net/10453/148467