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ON ROBUST EXPONENTIAL STABILITY OF A CLASS OF ATTRACTOR NEURAL NETWORKS

Abstract Robust Exponential stability of continuous-time attractor neural networks with delays is discussed. A new sufficient condition ensuring existence and uniqueness of periodic solution for a general class of interval dynamical systems are obtained. Discrete-time analogue of the continuous-time systems with periodic input is formulated and we study their dynamical characteristics. The robust exponential stability and periodicity of the continuous-time systems is preserved by the discrete-time analogue without any restriction imposed on the uniform discretization step-size.