The almost periodic solution of Lotka-Volterra recurrent neural networks with delays

Liu, Y; Liu, B; Ling, SH

The almost periodic solution of Lotka-Volterra recurrent neural networks with delays

Liu, Y Liu, B Ling, SH

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Publication Type:: Journal Article
Citation:: Neurocomputing, 2011, 74 (6), pp. 1062 - 1068
Issue Date:: 2011-02-15

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Field	Value	Language
dc.contributor.author	Liu, Y	en_US
dc.contributor.author	Liu, B	en_US
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.date.issued	2011-02-15	en_US
dc.identifier.citation	Neurocomputing, 2011, 74 (6), pp. 1062 - 1068	en_US
dc.identifier.issn	0925-2312	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15147
dc.description.abstract	By the fixed-point theorem subject to different polyhedrons and some inequalities (e.g., the inequality resulted from quadratic programming), we obtain three theorems for the Lotka-Volterra recurrent neural networks having almost periodic coefficients and delays. One of the three theorems can only ensure the existence of an almost periodic solution, whose existence and uniqueness the other two theorems are about. By using Lyapunov function, the sufficient condition guaranteeing the global stability of the solution is presented. Furthermore, two numerical examples are employed to illustrate the feasibility and validity of the obtained criteria. Compared with known results, the networks model is novel, and the results are extended and improved. © 2010 Elsevier B.V.	en_US
dc.relation.ispartof	Neurocomputing	en_US
dc.relation.isbasedon	10.1016/j.neucom.2010.11.009	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	The almost periodic solution of Lotka-Volterra recurrent neural networks with delays	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	74	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	17 Psychology and Cognitive Sciences	en_US
dc.location.activity	ISI:000287896600016
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/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	74	en_US

Abstract:

By the fixed-point theorem subject to different polyhedrons and some inequalities (e.g., the inequality resulted from quadratic programming), we obtain three theorems for the Lotka-Volterra recurrent neural networks having almost periodic coefficients and delays. One of the three theorems can only ensure the existence of an almost periodic solution, whose existence and uniqueness the other two theorems are about. By using Lyapunov function, the sufficient condition guaranteeing the global stability of the solution is presented. Furthermore, two numerical examples are employed to illustrate the feasibility and validity of the obtained criteria. Compared with known results, the networks model is novel, and the results are extended and improved. © 2010 Elsevier B.V.

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http://hdl.handle.net/10453/15147