In this paper, we study the problem of global exponential stability for cellular neural networks (CNN) with time-varying delays and fixed moments of impulsive effect. We establish several stability criteria by employing Lyapunov functions and the Razumikhin technique.
Global exponential stability and global attractivity of impulsive Hopfield neural networks with time delays
β Scribed by Xilin Fu; Xiaodi Li
- Publisher
- Elsevier Science
- Year
- 2009
- Tongue
- English
- Weight
- 1014 KB
- Volume
- 231
- Category
- Article
- ISSN
- 0377-0427
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β¦ Synopsis
In this paper, by means of constructing the extended impulsive delayed Halanay inequality and by Lyapunov functional methods, we analyze the global exponential stability and global attractivity of impulsive Hopfield neural networks with time delays. Some new sufficient conditions ensuring exponential stability of the unique equilibrium point of impulsive Hopfield neural networks with time delays are obtained. Those conditions are more feasible than that given in the earlier references to some extent. Some numerical examples are also discussed in this work to illustrate the advantage of the results we obtained.
π SIMILAR VOLUMES
In this paper, the problem of global exponential stability for cellular neural networks (CNNs) with time-varying delays and fixed moments of impulsive effect is studied. A new sufficient condition has been presented ensuring the global exponential stability of the equilibrium points by using piecewi
In this paper, by utilizing the Lyapunov functionals, the analysis method and the impulsive control, we analyze the exponential stability of Hopfield neural networks with time-varying delays. A new criterion on the exponential stabilization by impulses and the exponential stabilization by periodic i