Robust convergence of Cohen–Grossberg neural networks with time-varying delays

Abstract In this paper, robust convergence is studied for the Cohen–Grossberg neural networks (CGNNs) with time-varying delays. By applying the differential inequality and the Lyapunov method, some delay-independent conditions are derived ensuring the robust CGNNs to converge, globally, uniformly and exponentially, to a ball in the state space with a pre-specified convergence rate. Finally, the effectiveness of our results are verified by an illustrative example.

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