Paper Title:
Global Asymptotic Stability of Stochastic Fuzzy Cellular Neural Networks with Time-Varying Delays
  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.

  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, Y. H. Liu, H. L. Lan, "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
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$32.00
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