Nonlinear system identification

Stochastic approximation methods for the identification of parameters of nonlinear systems without dynamics have been widely discussed in the literature. In this work, two classes of discrete-time nonlinear dynamical systems driven by independent noise are considered. The measurements are assumed to be linear scalar and are made in the presence of independent noise. The systems under consideration are identified by the estimation of the parameters appearing in the evolution operator. These parameters are assumed to be constant during the identification time and they are estimated by means of stochastic approximation algorithms. A computer algorithm based on the above method is used to identify the parameters for some typical examples