New results on anti-synchronization of switched neural networks with time-varying delays and lag signals.

Cao, Y; Wen, S; Chen, MZQ; Huang, T; Zeng, Z

New results on anti-synchronization of switched neural networks with time-varying delays and lag signals.

Cao, Y Wen, S

Chen, MZQ Huang, T Zeng, Z

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Neural Netw, 2016, 81, pp. 52-58
Issue Date:: 2016-09

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Field	Value	Language
dc.contributor.author	Cao, Y
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.contributor.author	Chen, MZQ
dc.contributor.author	Huang, T
dc.contributor.author	Zeng, Z
dc.date.accessioned	2022-08-15T00:40:35Z
dc.date.available	2016-05-09
dc.date.available	2022-08-15T00:40:35Z
dc.date.issued	2016-09
dc.identifier.citation	Neural Netw, 2016, 81, pp. 52-58
dc.identifier.issn	0893-6080
dc.identifier.issn	1879-2782
dc.identifier.uri	http://hdl.handle.net/10453/160159
dc.description.abstract	This paper investigates the problem of global exponential anti-synchronization of a class of switched neural networks with time-varying delays and lag signals. Considering the packed circuits, the controller is dependent on the output of the system as the inner states are very hard to measure. Therefore, it is necessary to investigate the controller based on the output of the neuron cell. Through theoretical analysis, it is obvious that the obtained ones improve and generalize the results derived in the previous literature. To illustrate the effectiveness, a simulation example with applications in image encryptions is also presented in the paper.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation	National Natural Science Foundation of China61403152
dc.relation.ispartof	Neural Netw
dc.relation.isbasedon	10.1016/j.neunet.2016.05.004
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.mesh	Algorithms
dc.subject.mesh	Computer Security
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Neurons
dc.subject.mesh	Nonlinear Dynamics
dc.subject.mesh	Time Factors
dc.subject.mesh	Neurons
dc.subject.mesh	Algorithms
dc.subject.mesh	Nonlinear Dynamics
dc.subject.mesh	Time Factors
dc.subject.mesh	Computer Security
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Neural Networks, Computer
dc.title	New results on anti-synchronization of switched neural networks with time-varying delays and lag signals.
dc.type	Journal Article
utslib.citation.volume	81
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	*
dc.date.updated	2022-08-15T00:40:34Z
pubs.publication-status	Published
pubs.volume	81

Abstract:

This paper investigates the problem of global exponential anti-synchronization of a class of switched neural networks with time-varying delays and lag signals. Considering the packed circuits, the controller is dependent on the output of the system as the inner states are very hard to measure. Therefore, it is necessary to investigate the controller based on the output of the neuron cell. Through theoretical analysis, it is obvious that the obtained ones improve and generalize the results derived in the previous literature. To illustrate the effectiveness, a simulation example with applications in image encryptions is also presented in the paper.

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

http://hdl.handle.net/10453/160159