Attractivity analysis of memristor-Based cellular neural networks with time-Varying delays

Guo, Z; Wang, J; Yan, Z

Attractivity analysis of memristor-Based cellular neural networks with time-Varying delays

Guo, Z Wang, J Yan, Z

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Neural Networks and Learning Systems, 2014, 25 (4), pp. 704 - 717
Issue Date:: 2014-04-01

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Field	Value	Language
dc.contributor.author	Guo, Z	en_US
dc.contributor.author	Wang, J	en_US
dc.contributor.author	Yan, Z https://orcid.org/0000-0003-3368-2100	en_US
dc.date.issued	2014-04-01	en_US
dc.identifier.citation	IEEE Transactions on Neural Networks and Learning Systems, 2014, 25 (4), pp. 704 - 717	en_US
dc.identifier.issn	2162-237X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119086
dc.description.abstract	This paper presents new theoretical results on the invariance and attractivity of memristor-based cellular neural networks (MCNNs) with time-varying delays. First, sufficient conditions to assure the boundedness and global attractivity of the networks are derived. Using state-space decomposition and some analytic techniques, it is shown that the number of equilibria located in the saturation regions of the piecewise-linear activation functions of an n-neuron MCNN with time-varying delays increases significantly from 2 n to 22n2+n22n2(times}) compared with that without a memristor. In addition, sufficient conditions for the invariance and local or global attractivity of equilibria or attractive sets in any designated region are derived. Finally, two illustrative examples are given to elaborate the characteristics of the results in detail. © 2013 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Neural Networks and Learning Systems	en_US
dc.relation.isbasedon	10.1109/TNNLS.2013.2280556	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.subject.mesh	Nerve Net	en_US
dc.subject.mesh	Neurons	en_US
dc.subject.mesh	Animals	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Linear Models	en_US
dc.subject.mesh	Synaptic Transmission	en_US
dc.subject.mesh	Action Potentials	en_US
dc.subject.mesh	Models, Neurological	en_US
dc.subject.mesh	Computer Simulation	en_US
dc.title	Attractivity analysis of memristor-Based cellular neural networks with time-Varying delays	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	25	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
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 - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	25	en_US

Abstract:

This paper presents new theoretical results on the invariance and attractivity of memristor-based cellular neural networks (MCNNs) with time-varying delays. First, sufficient conditions to assure the boundedness and global attractivity of the networks are derived. Using state-space decomposition and some analytic techniques, it is shown that the number of equilibria located in the saturation regions of the piecewise-linear activation functions of an n-neuron MCNN with time-varying delays increases significantly from 2 n to 22n2+n22n2(times}) compared with that without a memristor. In addition, sufficient conditions for the invariance and local or global attractivity of equilibria or attractive sets in any designated region are derived. Finally, two illustrative examples are given to elaborate the characteristics of the results in detail. © 2013 IEEE.

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http://hdl.handle.net/10453/119086