Quantized synchronization of memristive neural networks with time-varying delays via super-twisting algorithm

Sun, B; Wen, S; Wang, S; Huang, T; Chen, Y; Li, P

Quantized synchronization of memristive neural networks with time-varying delays via super-twisting algorithm

Sun, B Wen, S

Wang, S Huang, T Chen, Y Li, P

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Neurocomputing, 2020, 380, pp. 133-140
Issue Date:: 2020-03-07

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Field	Value	Language
dc.contributor.author	Sun, B
dc.contributor.author	Wen, S https://orcid.org/0000-0002-5048-0319
dc.contributor.author	Wang, S
dc.contributor.author	Huang, T
dc.contributor.author	Chen, Y
dc.contributor.author	Li, P
dc.date.accessioned	2021-04-28T04:08:52Z
dc.date.available	2021-04-28T04:08:52Z
dc.date.issued	2020-03-07
dc.identifier.citation	Neurocomputing, 2020, 380, pp. 133-140
dc.identifier.issn	0925-2312
dc.identifier.issn	1872-8286
dc.identifier.uri	http://hdl.handle.net/10453/148450
dc.description.abstract	In this paper, we investigate quantized synchronization control problem of memristive neural networks (MNNs) with time-varying delays via super-twisting algorithm. A feedback controller is introduced with quantized method. To enormously reduce the computational complexity of the controller under super-twisting algorithm, two quantized control schemes are proposed with uniform quantizer and logarithmic quantization. We obtain some sufficient conditions of specific control plans to guarantee that the driving MNNs can synchronize with the response MNNs. A neoteric Lyapunov functional is designed to analyze the synchronization problem. Finally, in this paper ending, some illustrative examples are given in support of our results.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Neurocomputing
dc.relation.isbasedon	10.1016/j.neucom.2019.11.003
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 17 Psychology and Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Quantized synchronization of memristive neural networks with time-varying delays via super-twisting algorithm
dc.type	Journal Article
utslib.citation.volume	380
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	17 Psychology and Cognitive Sciences
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	2021-04-28T04:08:51Z
pubs.publication-status	Published
pubs.volume	380

Abstract:

In this paper, we investigate quantized synchronization control problem of memristive neural networks (MNNs) with time-varying delays via super-twisting algorithm. A feedback controller is introduced with quantized method. To enormously reduce the computational complexity of the controller under super-twisting algorithm, two quantized control schemes are proposed with uniform quantizer and logarithmic quantization. We obtain some sufficient conditions of specific control plans to guarantee that the driving MNNs can synchronize with the response MNNs. A neoteric Lyapunov functional is designed to analyze the synchronization problem. Finally, in this paper ending, some illustrative examples are given in support of our results.

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http://hdl.handle.net/10453/148450