Exponential Stability of a Class of Stochastic Interval Cellular Neural Networks

Author

Han, Jin-fang ; Qiu, Ji-qing

Author_Institution

Inst. of Eng. Math., Hebei Univ. of Sci. & Technol., Shijiazhuang, China

Volume

2

fYear

2009

fDate

7-8 Nov. 2009

Firstpage

530

Lastpage

534

Abstract

The exponential stability of a class of stochastic interval cellular neural networks with delay is investigated in this paper. For such neural networks, a kind of equivalent description is given ,and several sufficient conditions for the exponential stability in the mean square and surely exponential stability are established by the Lyapunov function method and lto formula. The criteria given here are generalizations of some provided in the earlier references.

Keywords

Lyapunov methods; asymptotic stability; cellular neural nets; delays; neurocontrollers; Lyapunov function; cellular neural networks; delays; exponential stability; lto formula; Artificial intelligence; Cellular neural networks; Computational intelligence; Indium tin oxide; Lyapunov method; Neural networks; Robust stability; Stability criteria; Stochastic processes; Sufficient conditions; Delay; Exponential Stability; Lyapunov function; Stochastic interval Cellular Neural Networks; formula;

fLanguage

English

Publisher

ieee

Conference_Titel

Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4244-3835-8

Electronic_ISBN

978-0-7695-3816-7

Type

conf

DOI

10.1109/AICI.2009.483

Filename

5375907