Quantized passification of delayed memristor-based neural networks via sliding model control

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

Quantized passification of delayed memristor-based neural networks via sliding model control

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

Wen, S

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Journal of the Franklin Institute, 2020, 357, (6), pp. 3741-3752
Issue Date:: 2020-04-01

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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	2021-04-28T06:18:14Z
dc.date.available	2021-04-28T06:18:14Z
dc.date.issued	2020-04-01
dc.identifier.citation	Journal of the Franklin Institute, 2020, 357, (6), pp. 3741-3752
dc.identifier.issn	0016-0032
dc.identifier.issn	0016-0032
dc.identifier.uri	http://hdl.handle.net/10453/148466
dc.description.abstract	In this paper, quantized passification is investigated for memristive neural networks (MNNs) with time-varying delays via sliding model control. The controller is designed with quantized schemes to reduce the computational complexity via uniform quantization and logarithmic quantizer. By choosing suitable Lyapunov functional and using LMI toolbox, some specific conditions are obtained to make MNN passive. At last, we give an illustrative example to ensure the correctness of the theorem.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Journal of the Franklin Institute
dc.relation.isbasedon	10.1016/j.jfranklin.2020.02.053
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0906 Electrical and Electronic Engineering
dc.subject.classification	Industrial Engineering & Automation
dc.title	Quantized passification of delayed memristor-based neural networks via sliding model control
dc.type	Journal Article
utslib.citation.volume	357
utslib.for	0102 Applied Mathematics
utslib.for	0906 Electrical and Electronic Engineering
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	2021-04-28T06:18:13Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	357
utslib.citation.issue	6

Abstract:

In this paper, quantized passification is investigated for memristive neural networks (MNNs) with time-varying delays via sliding model control. The controller is designed with quantized schemes to reduce the computational complexity via uniform quantization and logarithmic quantizer. By choosing suitable Lyapunov functional and using LMI toolbox, some specific conditions are obtained to make MNN passive. At last, we give an illustrative example to ensure the correctness of the theorem.

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http://hdl.handle.net/10453/148466