Abstract and Applied Analysis

Stochastic Passivity of Uncertain Neural Networks with Time-Varying Delays

Abstract

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.

Article information

Source
Abstr. Appl. Anal., Volume 2009 (2009), Article ID 725846, 16 pages.

Dates
First available in Project Euclid: 16 March 2010

https://projecteuclid.org/euclid.aaa/1268745621

Digital Object Identifier
doi:10.1155/2009/725846

Mathematical Reviews number (MathSciNet)
MR2576580

Zentralblatt MATH identifier
1184.93120

Citation

Zhou, Jianting; Song, Qiankun; Yang, Jianxi. Stochastic Passivity of Uncertain Neural Networks with Time-Varying Delays. Abstr. Appl. Anal. 2009 (2009), Article ID 725846, 16 pages. doi:10.1155/2009/725846. https://projecteuclid.org/euclid.aaa/1268745621

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