A learning algorithm for the dynamics of CNN with nonlinear templates. I. Discrete-time case

Abstract

A learning algorithm for the dynamics of discrete-time cellular neural networks (DTCNN) with gradient-based nonlinear templates is presented. For modeling the dynamics of nonlinear spatio-temporal systems with DTCNN, the algorithm is applied to find the network parameters. Results for two different nonlinear discrete-time systems are discussed in detail.