Paper Title:

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

Periodical Advanced Materials Research (Volumes 139 - 141)
Main Theme Manufacturing Engineering and Automation I
Edited by Liangchi Zhang, Chunliang Zhang and Tielin Shi
Pages 1714-1717
DOI 10.4028/www.scientific.net/AMR.139-141.1714
Citation Wen Guang Luo et al., 2010, Advanced Materials Research, 139-141, 1714
Online since October, 2010
Authors Wen Guang Luo, Yong Hua Liu, Hong Li Lan
Keywords Global Asymptotic Stability, Linear Matrix Inequality (LMI), Stochastic Fuzzy Cellular Neural Networks, Time-Varying Delay
Price US$ 28,-
Article Preview
View full size
Abstract

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.