Title :
Exponential Stability of Stochastic Interval Cellular Neural Networks
Author :
Han, Jinfang ; Liu, Zhiyong
Author_Institution :
Inst. ofEng. Math., Hebei Univ. of Sci. & Technol., 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 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; stochastic processes; Lyapunov function; Razumikhin theorem; exponential stability problem; norm inequalities; stochastic interval cellular neural networks; stochastic interval delayed cellular neural networks; sufficient condition; Artificial intelligence; Cellular networks; Cellular neural networks; Computer networks; Neural networks; Robust stability; Stability criteria; Stochastic processes; Stochastic systems; Sufficient conditions; Exponential Stability; Lyapunov function; Razumikhin theorems; Stochastic Cellular Neural Networks; formula;
Conference_Titel :
Natural Computation, 2009. ICNC '09. Fifth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3736-8
DOI :
10.1109/ICNC.2009.701
