A Recursive Prediction Error Parameter Estimator For Nonlinear Models

摘要

A recursive prediction error parameter estimation algorithm is derived for systems which can be represented by the NARMAX (Nonlinear ARMAX) model. A convergence analysis is presented using the differential equation approach and the new concept of m-invertibility is introduced. The analysis shows that while a highly nonlinear process model may be used to capture the nonlinearity of the system it is advisable to fit a simple noise model. The results of applying the algorithm to both simulated and real data are included.