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

Authors:
Jinhui Zhang;Peng Shi;Jiqing Qiu;Hongjiu Yang
Affiliations:
College of Sciences, Hebei University of Science and Technology, Shijiazhuang 050018, China and Department of Automatic Control, Beijing Institute of Technology, Beijing 100081, China;Faculty of Advanced Technology, University of Glamorgan, Pontypridd CF37 1DL, United Kingdom;College of Sciences, Hebei University of Science and Technology, Shijiazhuang 050018, China;College of Sciences, Hebei University of Science and Technology, Shijiazhuang 050018, China
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
2008

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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.