A new criterion for exponential stability of uncertain stochastic neural networks with mixed delays

Zhang, J.; Shi, P.; Qiu, J.; Yang, H.

doi:10.1016/j.mcm.2007.05.014

A new criterion for exponential stability of uncertain stochastic neural networks with mixed delays

Date

2008

Authors

Zhang, J.

Shi, P.

Qiu, J.

Yang, H.

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Journal article

Citation

Mathematical and Computer Modelling, 2008; 47(9-10):1042-1051

Statement of Responsibility

Jinhui Zhang, Peng Shi, Jiqing Qiu, Hongjiu Yang

DOI

10.1016/j.mcm.2007.05.014

Abstract

This paper deals with the problem of exponential stability for a class of uncertain stochastic neural networks with both discrete and distributed delays (also called mixed delays). The system possesses time-varying and norm-bounded uncertainties. Based on Lyapunov–Krasovskii functional and stochastic analysis approaches, new stability criteria are presented in terms of linear matrix inequalities to guarantee the delayed neural networks to be robustly exponentially stable in the mean square for all admissible parameter uncertainties. Numerical examples are given to illustrate the effectiveness of the developed techniques.

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https://doi.org/10.1016/j.mcm.2007.05.014

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http://hdl.handle.net/2440/83496

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A new criterion for exponential stability of uncertain stochastic neural networks with mixed delays

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