Global exponential dissipativity and stabilization of memristor-based recurrent neural networks with time-varying delays

Guo, Z; Wang, J; Yan, Z

Global exponential dissipativity and stabilization of memristor-based recurrent neural networks with time-varying delays

Guo, Z Wang, J Yan, Z

Publication Type:: Journal Article
Citation:: Neural Networks, 2013, 48 pp. 158 - 172
Issue Date:: 2013-12-01

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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.available	2013-08-20	en_US
dc.date.issued	2013-12-01	en_US
dc.identifier.citation	Neural Networks, 2013, 48 pp. 158 - 172	en_US
dc.identifier.issn	0893-6080	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119084
dc.description.abstract	This paper addresses the global exponential dissipativity of memristor-based recurrent neural networks with time-varying delays. By constructing proper Lyapunov functionals and using M-matrix theory and LaSalle invariant principle, the sets of global exponentially dissipativity are characterized parametrically. It is proven herein that there are 22n2-n equilibria for an n-neuron memristor-based neural network and they are located in the derived globally attractive sets. It is also shown that memristor-based recurrent neural networks with time-varying delays are stabilizable at the origin of the state space by using a linear state feedback control law with appropriate gains. Finally, two numerical examples are discussed in detail to illustrate the characteristics of the results. © 2013 Elsevier Ltd.	en_US
dc.relation.ispartof	Neural Networks	en_US
dc.relation.isbasedon	10.1016/j.neunet.2013.08.002	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.subject.mesh	Neurons	en_US
dc.subject.mesh	Linear Models	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Neural Networks (Computer)	en_US
dc.subject.mesh	Nonlinear Dynamics	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Linear Models	en_US
dc.subject.mesh	Neural Networks (Computer)	en_US
dc.subject.mesh	Neurons	en_US
dc.subject.mesh	Nonlinear Dynamics	en_US
dc.title	Global exponential dissipativity and stabilization of memristor-based recurrent neural networks with time-varying delays	en_US
dc.type	Journal Article
utslib.description.version	Published	en_US
utslib.citation.volume	48	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	MD Multidisciplinary	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.publication-status	Published	en_US
pubs.volume	48	en_US

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Abstract:

This paper addresses the global exponential dissipativity of memristor-based recurrent neural networks with time-varying delays. By constructing proper Lyapunov functionals and using M-matrix theory and LaSalle invariant principle, the sets of global exponentially dissipativity are characterized parametrically. It is proven herein that there are 22n2-n equilibria for an n-neuron memristor-based neural network and they are located in the derived globally attractive sets. It is also shown that memristor-based recurrent neural networks with time-varying delays are stabilizable at the origin of the state space by using a linear state feedback control law with appropriate gains. Finally, two numerical examples are discussed in detail to illustrate the characteristics of the results. © 2013 Elsevier Ltd.

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http://hdl.handle.net/10453/119084