Stability analysis of stochastic recurrent neural networks with unbounded time-varying delays

Authors:
Xuejing Meng;Maosheng Tian;Shigeng Hu
Affiliations:
School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan 430074, PR China;State Engineering Research Center of Numerical Control System, Huazhong University of Science and Technology, Wuhan 430074, PR China;School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan 430074, PR China
Venue:
Neurocomputing
Year:
2011

Citing 12
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Abstract

In this paper, the stability analysis issue of stochastic recurrent neural networks with unbounded time-varying delays is investigated. By the idea of Lyapunov function and the semi-martingale convergence theorem, both pth moment exponential stability and almost sure exponential stability are obtained. Moreover, the M-matrix technique is borrowed to make the results more applicable. Our criteria can be used not only in the case of bounded delay but also in the case of unbounded delay. Some earlier results are improved and generalized. An example is also given to demonstrate our results.