Passivity and passification of memristor-based recurrent neural networks with time-varying delays

Guo, Z; Wang, J; Yan, Z

Passivity and passification of memristor-based recurrent 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 (11), pp. 2099 - 2109
Issue Date:: 2014-11-01

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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	2014-11-01	en_US
dc.identifier.citation	IEEE Transactions on Neural Networks and Learning Systems, 2014, 25 (11), pp. 2099 - 2109	en_US
dc.identifier.issn	2162-237X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119085
dc.description.abstract	© 2014 IEEE. This paper presents new theoretical results on the passivity and passification of a class of memristor-based recurrent neural networks (MRNNs) with time-varying delays. The casual assumptions on the boundedness and Lipschitz continuity of neuronal activation functions are relaxed. By constructing appropriate Lyapunov-Krasovskii functionals and using the characteristic function technique, passivity conditions are cast in the form of linear matrix inequalities (LMIs), which can be checked numerically using an LMI toolbox. Based on these conditions, two procedures for designing passification controllers are proposed, which guarantee that MRNNs with time-varying delays are passive. Finally, two illustrative examples are presented to show the characteristics of the main results in detail.	en_US
dc.relation.ispartof	IEEE Transactions on Neural Networks and Learning Systems	en_US
dc.relation.isbasedon	10.1109/TNNLS.2014.2305440	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Reaction Time	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	Time Factors	en_US
dc.subject.mesh	Neural Networks, Computer	en_US
dc.title	Passivity and passification of memristor-based recurrent neural networks with time-varying delays	en_US
dc.type	Journal Article
utslib.citation.volume	11	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	11	en_US
pubs.publication-status	Published	en_US
pubs.volume	25	en_US

Abstract:

© 2014 IEEE. This paper presents new theoretical results on the passivity and passification of a class of memristor-based recurrent neural networks (MRNNs) with time-varying delays. The casual assumptions on the boundedness and Lipschitz continuity of neuronal activation functions are relaxed. By constructing appropriate Lyapunov-Krasovskii functionals and using the characteristic function technique, passivity conditions are cast in the form of linear matrix inequalities (LMIs), which can be checked numerically using an LMI toolbox. Based on these conditions, two procedures for designing passification controllers are proposed, which guarantee that MRNNs with time-varying delays are passive. Finally, two illustrative examples are presented to show the characteristics of the main results in detail.

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http://hdl.handle.net/10453/119085