Global Stabilization of Memristive Neural Networks with Leakage and Time-Varying Delays Via Quantized Sliding-Mode Controller

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

Global Stabilization of Memristive Neural Networks with Leakage and Time-Varying Delays Via Quantized Sliding-Mode Controller

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

Wen, S

Permalink

Publisher:: Springer Science and Business Media LLC
Publication Type:: Journal Article
Citation:: Neural Processing Letters, 2020, 52, (3), pp. 2451-2468
Issue Date:: 2020-12-01

Closed Access

	Filename	Description	Size
	Cao2020_Article_GlobalStabilizationOfMemristiv.pdf	Published version	689.41 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Cao, Y
dc.contributor.author	Sun, B
dc.contributor.author	Guo, Z
dc.contributor.author	Huang, T
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	2021-01-31T21:59:37Z
dc.date.available	2021-01-31T21:59:37Z
dc.date.issued	2020-12-01
dc.identifier.citation	Neural Processing Letters, 2020, 52, (3), pp. 2451-2468
dc.identifier.issn	1370-4621
dc.identifier.issn	1573-773X
dc.identifier.uri	http://hdl.handle.net/10453/145683
dc.description.abstract	© 2020, Springer Science+Business Media, LLC, part of Springer Nature. This pape investigates the global stabilization of memristive neural networks (MNNs) with leakage and time-varying delays via quantized sliding-mode controller. The leakage delay is considered in the MNNs. Sliding mode controller is imported to ensure global stabilization of delayed MNNs. We also introduce two quantization schemes with uniform quantizer and logarithmic quantizer. Our goal is to deal with errors before and after quantization. We give some simulations and comparisons between two quantizers in the end of this paper.
dc.language	en
dc.publisher	Springer Science and Business Media LLC
dc.relation.ispartof	Neural Processing Letters
dc.relation.isbasedon	10.1007/s11063-020-10356-y
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Global Stabilization of Memristive Neural Networks with Leakage and Time-Varying Delays Via Quantized Sliding-Mode Controller
dc.type	Journal Article
utslib.citation.volume	52
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	1702 Cognitive Sciences
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
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2021-01-31T21:59:29Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	52
utslib.citation.issue	3

Abstract:

© 2020, Springer Science+Business Media, LLC, part of Springer Nature. This pape investigates the global stabilization of memristive neural networks (MNNs) with leakage and time-varying delays via quantized sliding-mode controller. The leakage delay is considered in the MNNs. Sliding mode controller is imported to ensure global stabilization of delayed MNNs. We also introduce two quantization schemes with uniform quantizer and logarithmic quantizer. Our goal is to deal with errors before and after quantization. We give some simulations and comparisons between two quantizers in the end of this paper.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/145683