Abstract
In this paper, the global exponential stability is discussed for static recurrent neural networks. Without assuming the boundedness, monotonicity and differentiability of the activation functions, a new sufficient condition is obtained to ensure the existence and uniqueness of the equilibrium based on the nonlinear measure. Meanwhile, the condition obtained also guarantees the global exponential stability of the delayed neural networks via constructing a proper Lyapunov functional. The results, which are independent of the time delay, can be checked easily by convex optimization algorithms. In the end of this paper, two illustrative examples are also given to show the effectiveness of our results.
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