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

Magdi S. Mahmoud and Fouad M. AL Sunni
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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

International Journal of System Dynamics Applications (IJSDA), 2012, vol. 1, issue 2, 1-14

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.

Date: 2012
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