Abstract and Applied Analysis
Volume 2009 (2009), Article ID 725846, 16 pages
Research Article

Stochastic Passivity of Uncertain Neural Networks with Time-Varying Delays

1College of Civil Engineering and Architecture, Chongqing Jiaotong University, Chongqing 400074, China
2Department of Mathematics, Chongqing Jiaotong University, Chongqing 400074, China

Received 22 July 2009; Accepted 18 October 2009

Academic Editor: Elena Litsyn

Copyright © 2009 Jianting Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The passivity problem is investigated for a class of stochastic uncertain neural networks with time-varying delay as well as generalized activation functions. By constructing appropriate Lyapunov-Krasovskii functionals, and employing Newton-Leibniz formulation, the free-weighting matrix method, and stochastic analysis technique, a delay-dependent criterion for checking the passivity of the addressed neural networks is established in terms of linear matrix inequalities (LMIs), which can be checked numerically using the effective LMI toolbox in MATLAB. An example with simulation is given to show the effectiveness and less conservatism of the proposed criterion. It is noteworthy that the traditional assumptions on the differentiability of the time-varying delays and the boundedness of its derivative are removed.