Title :
Global exponential stability of a class of impulsive Cohen-Grossberg neural networks with unbounded delays
Author :
Xu, Xiaohui ; Zhang, Keyue ; Keyue Zhang
Author_Institution :
Nat. Traction Power Lab., Southwest Jiaotong Univ., Chengdu, China
Abstract :
This paper studies the problems of global exponential stability for a class of impulsive Cohen-Grossberg neural networks, which contains both variable and unbounded delays. By constructing vector Lyapunov functions and nonlinear integro-differential inequalities, the sufficient conditions for global exponential stability are obtained. An example is given to demonstrate the effectiveness of the proposed conditions.
Keywords :
Lyapunov methods; asymptotic stability; delays; integro-differential equations; neural nets; nonlinear equations; global exponential stability; impulsive Cohen-Grossberg neural networks; nonlinear integrodifferential inequalities; sufficient conditions; unbounded delays; variable delays; vector Lyapunov functions; Cellular neural networks; Delay effects; Hopfield neural networks; Intelligent networks; Intelligent systems; Knowledge engineering; Laboratories; Neural networks; Stability; Sufficient conditions;
Conference_Titel :
Intelligent System and Knowledge Engineering, 2008. ISKE 2008. 3rd International Conference on
Conference_Location :
Xiamen
Print_ISBN :
978-1-4244-2196-1
Electronic_ISBN :
978-1-4244-2197-8
DOI :
10.1109/ISKE.2008.4731048
