Exponential synchronization of memristive neural networks with time-varying delays via quantized sliding-mode control.

Sun, B; Wang, S; Cao, Y; Guo, Z; Huang, T; Wen, S

Exponential synchronization of memristive neural networks with time-varying delays via quantized sliding-mode control.

Sun, B Wang, S Cao, Y Guo, Z Huang, T Wen, S

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Neural networks : the official journal of the International Neural Network Society, 2020, 126, pp. 163-169
Issue Date:: 2020-06

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Field	Value	Language
dc.contributor.author	Sun, B
dc.contributor.author	Wang, S
dc.contributor.author	Cao, Y
dc.contributor.author	Guo, Z
dc.contributor.author	Huang, T
dc.contributor.author	Wen, S https://orcid.org/0000-0002-5048-0319
dc.date.accessioned	2021-04-23T05:23:35Z
dc.date.available	2020-03-12
dc.date.available	2021-04-23T05:23:35Z
dc.date.issued	2020-06
dc.identifier.citation	Neural networks : the official journal of the International Neural Network Society, 2020, 126, pp. 163-169
dc.identifier.issn	0893-6080
dc.identifier.issn	1879-2782
dc.identifier.uri	http://hdl.handle.net/10453/148316
dc.description.abstract	In the paper, exponential synchronization issue is considered for memristive neural networks (MNNs) with time-varying delays via quantized sliding-mode algorithm. Quantized Sliding-mode controller is introduced to ensure the slave system can be exponentially synchronized with the host system via the super-twisting algorithm, which has been proved in the main results. Quantization function consists of uniform quantizer and logarithmic quantizer. Simulation results are given with comparisons between two quantizers in the end.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Neural networks : the official journal of the International Neural Network Society
dc.relation.isbasedon	10.1016/j.neunet.2020.03.014
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	Time Factors
dc.subject.mesh	Algorithms
dc.subject.mesh	Time Factors
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Algorithms
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Time Factors
dc.title	Exponential synchronization of memristive neural networks with time-varying delays via quantized sliding-mode control.
dc.type	Journal Article
utslib.citation.volume	126
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	*
pubs.consider-herdc	false
dc.date.updated	2021-04-23T05:23:34Z
pubs.publication-status	Published
pubs.volume	126

Abstract:

In the paper, exponential synchronization issue is considered for memristive neural networks (MNNs) with time-varying delays via quantized sliding-mode algorithm. Quantized Sliding-mode controller is introduced to ensure the slave system can be exponentially synchronized with the host system via the super-twisting algorithm, which has been proved in the main results. Quantization function consists of uniform quantizer and logarithmic quantizer. Simulation results are given with comparisons between two quantizers in the end.

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http://hdl.handle.net/10453/148316