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Global exponential stability of impulsive discrete-time neural networks with time-varying delays |
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| Authors: | Honglei Xu Yuanqiang Chen |
| Institution: | a Department of Mathematics, Guizhou University, Guiyang 550025, China b Department of Mathematics and Statistics, Curtin University of Technology, Perth, WA 6845, Australia c Department of Mathematics, National Minorities College of Guizhou, Guiyang 550025, China |
| 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. |
| Keywords: | Impulsive discrete-time neural networks Global exponential stability Exponential convergence rate Halanay inequality |
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