Novel delay-dependent robust stability criteria for neutral stochastic delayed neural networks

Authors:
Huabin Chen;Yong Zhang;Peng Hu
Affiliations:
Department of Mathematics, School of Science, Nanchang University, Nanchang 330031, PR China;College of Information Science and Technology, Wuhan University of Science and Technology, Wuhan 430081, PR China;School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan 430074, PR China
Venue:
Neurocomputing
Year:
2010

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Abstract

This paper is concerned with the globally exponential stability in mean square and almost surely exponential stability for neutral stochastic delayed neural networks. By constructing an appropriate Lyapunov-Krasovskii functional and with the help of the semimartingale convergence theorem, some delay-dependent sufficient conditions to guarantee the globally exponential stability in mean square and almost surely exponential stability of such systems are obtained in terms of the linear matrix inequality (LMI), which can be regarded as some less conservative criteria than some existing results when stochastic delayed neural networks of neutral type are designed. Finally, two illustrative numerical examples are given to demonstrate the advantages and applicabilities of the proposed results.