Application of feedforward neural networks to dynamical system identification and control

Abstract

Methods for identification and control of dynamical systems by adalines, two-layer, and three-layer feedforward neural networks (FNNs) using generalized weight adaptation algorithms are discussed. The FNNs considered contain odd nonlinear operators in both the neurons and the weight adaptation algorithms. Two application examples, each involving a nonlinear dynamical system, are considered. The first is identification of the system's forward and inverse dynamics. The second is control of the system using coordination of feedforward and feedback control combined with inverse system dynamics identification. Simulation results are used to verify the method's feasibility and to examine the effect of ENN parameter changes. Specifically the effect that the type of nonlinear activation functions present in the neurons and the type of nonlinear functions present in the weight adaptation algorithms have on FNN system dynamics identification performance is investigated.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>