Globally exponential stability of a class of impulsive neural networks with variable delays

Authors:
Jianfu Yang;Fengjian Yang;Chaolong Zhang;Dongqing Wu;Chuanxiang Gao
Affiliations:
Department of Computation Science, Zhongkai University of Agriculture and Engineering, Guangzhou, P. R China;Department of Computation Science, Zhongkai University of Agriculture and Engineering, Guangzhou, P. R China;Department of Computation Science, Zhongkai University of Agriculture and Engineering, Guangzhou, P. R China;Department of Computation Science, Zhongkai University of Agriculture and Engineering, Guangzhou, P. R China;Department of Computation Science, Zhongkai University of Agriculture and Engineering, Guangzhou, P. R China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

Citing 5
Cited 0

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Abstract

The main purpose of this paper is to study the globally exponential stability of the equilibrium point for a class of impulsive neural networks with time-varying delays. Without assuming global Lipschitz conditions on the activation functions, applying idea of vector Lyapunov function, combining Young inequality and Halanay differential inequality with delay, the sufficient conditions for globally exponential stability of neural networks are obtained.