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. Discretetime 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.