Global Asymptotic Stability of Stochastic Fuzzy Cellular Neural Networks with Time-Varying Delays

Abstract:

Article Preview

In this paper, the problem of global asymptotic stability in the mean square for stochastic fuzzy cellular neural networks (SFCNN) with time-varying delays is investigated. By constructing a newly proposed Lyapunov-Krasovskii function (LKF) and using Ito’s stochastic stability theory, a novel delay-dependent stability criterion is derived. The obtained stability result is helpful to design the stability of fuzzy cellular neural networks (FCNN) with time-varying delays when stochastic noise is taken into consideration. Since it is presented in terms of a linear matrix inequality (LMI), the sufficient condition is easy to be checked efficiently by utilizing some standard numerical packages such as the LMI Control Toolbox in Matlab. Finally, an illustrate example is given to verify the feasibility and usefulness of the proposed result.

Info:

Periodical:

Advanced Materials Research (Volumes 139-141)

Edited by:

Liangchi Zhang, Chunliang Zhang and Tielin Shi

Pages:

1714-1717

DOI:

10.4028/www.scientific.net/AMR.139-141.1714

Citation:

W. G. Luo et al., "Global Asymptotic Stability of Stochastic Fuzzy Cellular Neural Networks with Time-Varying Delays", Advanced Materials Research, Vols. 139-141, pp. 1714-1717, 2010

Online since:

October 2010

Export:

Price:

$35.00

In order to see related information, you need to Login.

In order to see related information, you need to Login.