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Improved Delay-Dependent Stability Analysis for Neural Networks with Interval Time-Varying Delays

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  • Jun-kang Tian
  • Yan-min Liu

Abstract

The problem of delay-dependent asymptotic stability analysis for neural networks with interval time-varying delays is considered based on the delay-partitioning method. Some less conservative stability criteria are established in terms of linear matrix inequalities (LMIs) by constructing a new Lyapunov-Krasovskii functional (LKF) in each subinterval and combining with reciprocally convex approach. Moreover, our criteria depend on both the upper and lower bounds on time-varying delay and its derivative, which is different from some existing ones. Finally, a numerical example is given to show the improved stability region of the proposed results.

Suggested Citation

  • Jun-kang Tian & Yan-min Liu, 2015. "Improved Delay-Dependent Stability Analysis for Neural Networks with Interval Time-Varying Delays," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-10, September.
    • Handle: RePEc:hin:jnlmpe:705367
    DOI: 10.1155/2015/705367
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    Cited by:

    1. Zamart, Chantapish & Botmart, Thongchai & Weera, Wajaree & Charoensin, Suphachai, 2022. "New delay-dependent conditions for finite-time extended dissipativity based non-fragile feedback control for neural networks with mixed interval time-varying delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 201(C), pages 684-713.

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