Wavelet-based adaptive robust M-estimator for nonlinear system identification

A wavelet-based robust M-estimation method for the identification of nonlinear systems is proposed. Because it is not based on the assumption that there is the class of error distribution, it takes a flexible, nonparametric approach and has the advantage of directly estimating the error distribution from the data, This M-estimator is optimal over any error distribution in the sense of maximum likelihood estimation. A Monte-Carlo study on a nonlinear chemical engineering example was used to compare the results with various previously utilized methods.