New Results on Robust Exponential Stability of Uncertain Stochastic Neural Networks with Mixed Time-Varying Delays

Authors:
Mingang Hua;Xinzhi Liu;Feiqi Deng;Juntao Fei
Affiliations:
College of Computer and Information, Hohai University, Changzhou, China 213022 and Department of Applied Mathematics, University of Waterloo, Waterloo, Canada N2L 3G1;Department of Applied Mathematics, University of Waterloo, Waterloo, Canada N2L 3G1;College of Automation Science and Engineering, South China University of Technology, Guangzhou, China 510640;College of Computer and Information, Hohai University, Changzhou, China 213022
Venue:
Neural Processing Letters
Year:
2010

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Abstract

This letter considers the robust exponential stability of uncertain stochastic neural networks with mixed time-varying delays. By using Lyapunov---Krasovskii functional and Itô's differential formula, several new sufficient conditions guaranteeing the global robust exponential stability are derived in terms of linear matrix inequalities. Numerical examples are given to illustrate the effectiveness and less conservativeness of our results.