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Global exponential stability of impulsive discrete-time neural networks with time-varying delays

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Open access
Authors
Xu, Honglei
Chen, Y.
Teo, Kok Lay
Date
2010
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Type
Journal Article
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Abstract

This paper studies the problem of global exponential stability and exponential convergence rate for a class of impulsive discrete-time neural networks with time-varying delays. Firstly, by means of the Lyapunov stability theory, some inequality analysis techniques and a discrete-time Halanay-type inequality technique, sufficient conditions for ensuring global exponential stability of discrete-time neural networks are derived, and the estimated exponential convergence rate is provided as well. The obtained results are then applied to derive global exponential stability criteria and exponential convergence rate of impulsive discrete-time neural networks with time-varying delays. Finally, numerical examples are provided to illustrate the effectiveness and usefulness of the obtained criteria.

Citation
Xu, Honglei and Chen, Yuanqiang and Teo, Kok Lay. 2010. Global exponential stability of impulsive discrete-time neural networks with time-varying delays. Applied Mathematics and Computations. 217 (2): pp. 537-544.
Source Title
Applied Mathematics and Computations
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DOI
Department
Department of Mathematics and Statistics
Remarks

NOTICE: This is the author’s version of a work that was accepted for publication in Applied Mathematics and Computation. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Applied Mathematics and Computation [217, 2, 2010] DOI 10.1016/j.amc.2010.05.087