Title :
Exponential p-stability of impulsive stochastic Cohen-Grossberg neural networks with variable coefficients and mixed delays
Author :
Yao, Xiaojie ; Qin, Fajin
Author_Institution :
Dept. of Math. & Comput. Sci., Liuzhou Teachers Coll., Liuzhou, China
Abstract :
In this paper, we consider a class of impulsive stochastic Cohen-Grossberg neural networks with variable coefficients and mixed delays. By establishing an L-operator differential inequality and using stochastic analysis technique, we obtain the exponential p-stability of the impulsive stochastic cohen-Grossberg neural networks with variable coefficients and mixed delays. These results are new and generalize a few pervious known results.
Keywords :
asymptotic stability; delays; neural nets; stochastic processes; L-operator differential inequality; exponential p-stability; impulsive stochastic Cohen-Grossberg neural networks; mixed delays; stochastic analysis technique; variable coefficients; Artificial neural networks; Delay; Stability analysis; Stochastic processes; Transmission line matrix methods; Vectors; Exponential p-stability; Impulsive Stochastic Cohen-Grossberg Neural Networks; L-operator Differential Inequality; Mixed Delays; Variable Coefficients;
Conference_Titel :
Control Conference (CCC), 2012 31st Chinese
Conference_Location :
Hefei
Print_ISBN :
978-1-4673-2581-3
