Title :
Exponential stability of stochastic interval cellular neural networks with delays
Author :
Han, Jin-fang ; Li, Fa-chao
Author_Institution :
Inst. of Eng. Math., Hebei Univ. of Sci. & Technilogy, Shijiazhuang, China
Abstract :
In this paper, the exponential stability problem of a class of stochastic interval delayed cellular neural networks is studied. Firstly, a kind of equivalent description of this stochastic interval delayed cellular neural networks is presented. Then by using the Ito formula, Razumikhin theorems, Lyapunov function and norm inequalities, several simple sufficient conditions are obtained which guarantee the exponential stability of the stochastic interval cellular neural networks, and some recent results reported in the literature are generalized.
Keywords :
Lyapunov methods; asymptotic stability; cellular neural nets; delay systems; neurocontrollers; stochastic systems; Ito formula; Lyapunov function; Razumikhin theorem; exponential stability; norm inequalities; stochastic interval delayed cellular neural network; Cellular networks; Cellular neural networks; Indium tin oxide; Lyapunov method; Neural networks; Robust stability; Stability criteria; Stochastic processes; Stochastic systems; Sufficient conditions; Delays; Exponential Stability; Itô formula; Lyapunov function; Stochastic Cellular Neural Networks;
Conference_Titel :
Wavelet Analysis and Pattern Recognition, 2009. ICWAPR 2009. International Conference on
Conference_Location :
Baoding
Print_ISBN :
978-1-4244-3728-3
Electronic_ISBN :
978-1-4244-3729-0
DOI :
10.1109/ICWAPR.2009.5207427
