LEARNING FIXED-POINT PATTERNS BY RECURRENT NETWORKS

Several learning algorithms have been derived for equilibrium points in recurrent neural networks. In this paper, we also consider learning the equilibrium points of such dynamical systems. We derive a structurally simple learning algorithm for recurrent networks which does not involve computing the trajectories of the system and we prove convergence and give examples. We also discuss solving for the connection weight matrix by iterative learning algorithms or direct solving. (C) 1994 Academic Press, Inc.