Fixed-time synchronization of delayed Cohen-Grossberg neural networks based on a novel sliding mode.

Xiao, J; Zeng, Z; Wu, A; Wen, S

Fixed-time synchronization of delayed Cohen-Grossberg neural networks based on a novel sliding mode.

Xiao, J Zeng, Z Wu, A Wen, S

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Neural networks : the official journal of the International Neural Network Society, 2020, 128, pp. 1-12
Issue Date:: 2020-08

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Field	Value	Language
dc.contributor.author	Xiao, J
dc.contributor.author	Zeng, Z
dc.contributor.author	Wu, A
dc.contributor.author	Wen, S https://orcid.org/0000-0002-5048-0319
dc.date.accessioned	2021-01-07T05:51:06Z
dc.date.available	2020-04-20
dc.date.available	2021-01-07T05:51:06Z
dc.date.issued	2020-08
dc.identifier.citation	Neural networks : the official journal of the International Neural Network Society, 2020, 128, pp. 1-12
dc.identifier.issn	0893-6080
dc.identifier.issn	1879-2782
dc.identifier.uri	http://hdl.handle.net/10453/145180
dc.description.abstract	This paper has discussed fixed-time synchronization of discontinuous Cohen-Grossberg neural networks with time-varying delays and matched disturbances based on sliding mode control technology. First, a novel sliding-mode surface is established. And, the dynamics on the sliding-mode surface can be achieved in the fixed time by employing the Gudermannian function. Then, considering the effect of delay, two different control schemes are introduced to ensure the fixed time reachability of the sliding mode. In addition, some useful criteria are given out for fixed-time synchronization of neural networks, and the setting time is formulated in a straightforward way. Finally, some examples and simulations are presented to verify the validity of the proposed results.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Elsevier BV
dc.relation.ispartof	Neural networks : the official journal of the International Neural Network Society
dc.relation.isbasedon	10.1016/j.neunet.2020.04.020
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.mesh	Time Factors
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Time Factors
dc.title	Fixed-time synchronization of delayed Cohen-Grossberg neural networks based on a novel sliding mode.
dc.type	Journal Article
utslib.citation.volume	128
utslib.location.activity	United States
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	2021-01-07T05:50:56Z
pubs.publication-status	Published
pubs.volume	128

Abstract:

This paper has discussed fixed-time synchronization of discontinuous Cohen-Grossberg neural networks with time-varying delays and matched disturbances based on sliding mode control technology. First, a novel sliding-mode surface is established. And, the dynamics on the sliding-mode surface can be achieved in the fixed time by employing the Gudermannian function. Then, considering the effect of delay, two different control schemes are introduced to ensure the fixed time reachability of the sliding mode. In addition, some useful criteria are given out for fixed-time synchronization of neural networks, and the setting time is formulated in a straightforward way. Finally, some examples and simulations are presented to verify the validity of the proposed results.

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http://hdl.handle.net/10453/145180