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Delay-dependent exponential stability for uncertain neutral stochastic neural networks with interval time-varying delay

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  • Huabin Chen
  • Yang Zhao

Abstract

This paper is mainly concerned with the problem for the robustly exponential stability in mean square moment of uncertain neutral stochastic neural networks with interval time-varying delay. With an appropriate augmented Lyapunov–Krasovskii functional (LKF) formulated, the convex combination method is utilised to estimate the derivative of the LKF. Some new delay-dependent exponential stability criteria for such systems are obtained in terms of linear matrix inequalities, which involve fewer matrix variables and have less conservatism. Finally, two illustrative numerical examples are given to show the effectiveness of our obtained results.

Suggested Citation

  • Huabin Chen & Yang Zhao, 2015. "Delay-dependent exponential stability for uncertain neutral stochastic neural networks with interval time-varying delay," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(14), pages 2584-2597, October.
    • Handle: RePEc:taf:tsysxx:v:46:y:2015:i:14:p:2584-2597
    DOI: 10.1080/00207721.2013.874507
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    1. Park, Ju H. & Kwon, O.M., 2009. "Global stability for neural networks of neutral-type with interval time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 41(3), pages 1174-1181.
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    Cited by:

    1. Rathinasamy, Anandaraman & Mayavel, Pichamuthu, 2023. "The balanced split step theta approximations of stochastic neutral Hopfield neural networks with time delay and Poisson jumps," Applied Mathematics and Computation, Elsevier, vol. 455(C).

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