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Stability of Discrete Recurrent Neural Networks with Interval Delays: Global Results

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  • Magdi S. Mahmoud

    (King Fahd University of Petroleum and Minerals, Saudi Arabia)

  • Fouad M. AL Sunni

    (King Fahd University of Petroleum and Minerals, Saudi Arabia)

Abstract

A global exponential stability method for a class of discrete time recurrent neural networks with interval time-varying delays and norm-bounded time-varying parameter uncertainties is developed in this paper. The method is derived based on a new Lyapunov-Krasovskii functional to exhibit the delay-range-dependent dynamics and to compensate for the enlarged time-span. In addition, it eliminates the need for over bounding and utilizes smaller number of LMI decision variables. Effective solutions to the global stability problem are provided in terms of feasibility-testing of parameterized linear matrix inequalities (LMIs). Numerical examples are presented to demonstrate the potential of the developed technique.

Suggested Citation

  • Magdi S. Mahmoud & Fouad M. AL Sunni, 2012. "Stability of Discrete Recurrent Neural Networks with Interval Delays: Global Results," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 1(2), pages 1-14, April.
    • Handle: RePEc:igg:jsda00:v:1:y:2012:i:2:p:1-14
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