Global asymptotic stability of Discrete-Time Cellular Neural Networks

Abstract

This paper presents two sufficient conditions for global stability of Discrete-Time Cellular Neural Networks (DTCNNs). It is shown that if the first or second norm of the feedback matrix is smaller than one, then a DTCNN converges to a unique and globally asymptotically stable equilibrium point for every external input.