Global Asymptotic Stability of Recurrent Neural Networks with Time Varying Delays

In this paper, two sufficient conditions are established for the global asymptotic stability of recurrent neural networks with multiple time varying delays. The Lyapunov-Krasovskii stability theory for functional differential equations and the linear matrix inequality approach are employed in our investigation. Our results are shown to be generalizations of some previously published results and are less conservative than existing results. The present results are also applicable to recurrent neural networks with constant time delays.

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