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

Volume

1

fYear

2009

fDate

14-16 Aug. 2009

Firstpage

144

Lastpage

148

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation, 2009. ICNC '09. Fifth International Conference on

Conference_Location

Tianjin

Print_ISBN

978-0-7695-3736-8

Type

conf

DOI

10.1109/ICNC.2009.701

Filename

5362565