Global learning algorithms for discrete-time cellular neural networks

Abstract

Two learning algorithms for discrete-time cellular neural networks (DTCNNs) are proposed, which do not require the a priori knowledge of the output trajectory of the network. A cost function is defined, which is minimized by direct search optimization methods and simulated annealing.<>