Parametric identification of nonlinear dynamic systems based on nonlinear crosscorrelation functions

The paper introduces the two-step identification method of least-squares parameter estimation based on correlation functions, for nonlinear dynamic systems with linear parameters. It deals with the identification of the input/output parameter models working in open- and closed-loops. The relation between the structure of a model, the choice of multipliers and the necessary shifting time domain are demonstrated. The effect of the noise to the estimation results are shown. The theoretical considerations are illustrated by digital and hybrid simulations and by the identification of a heat exchanger.