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

fYear

2009

fDate

12-15 July 2009

Firstpage

175

Lastpage

179

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/ICWAPR.2009.5207427

Filename

5207427