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Global Stability for Stochastic Interval Neural Networks with Mixed Time Delays

  • Junxiang Lu
  • Hong Xue
  • Shanshan Wang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 154)

Abstract

The globally stochastically asymptotic stability is considered for a class of stochastic interval neural networks with mixed delays (SIDNN) in this paper, which consist of both the discrete and distributed time delays. By introducing an equivalent transformation of interval matrices, a criterion on globally stochastically asymptotic stability is established. Based on an Lyapunov function and stochastic analysis theory, a linear matrix (LMI) approach is developed to derive the sufficient condition guaranteeing the globally stochastically asymptotic stability of the equilibrium point, where the feasibility of LMIs can be easily solved by the LMI-Toolbox in Matlab, and all the delayed differential equations are calculated numerically via DDE23 Program in Matlab.

Keywords

Asymptotic Stability Robust Stability Hopfield Neural Network Stochastic Neural Network Interval Matrice 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Notes

Acknowledgments

This work was supported by Shanxi Province Science Foundation for Youths,China(No. 2010JQ1016), the Science Research Foundation of Shaanxi Province Department of Education, China(No. 2010JK560).

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Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.School of ScienceXi’an Polytechnic UniversityXianPeople’s Republic of China

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