Adaptive identification and control of dynamical systems using neural networks

Abstract

In recent years, multilayer neural networks and recurrent networks have emerged as important components for representing nonlinear transformations and have proved particularly successful in pattern recognition and optimization problems. The authors explore methods for incorporating such networks in adaptive systems for the identification and control of complex nonlinear dynamical systems. >