On Nonparametric Identification of Wiener Systems with Deterministic Inputs
Simone Urbano, Eric Chaumette, Philippe Goupil, Jean-Yves Tourneret
Abstract
The identification of nonlinear Wiener models (NWMs) for deterministic inputs and Gaussian noise is studied. We show that the nonparametric kernel regression estimation of the nonlinearity of a NWM (based on the Nadaraya-Watson kernel estimator) can be formulated as a parametric estimation problem leading to a Gaussian conditional observation model. This property allows us to derive the maximum likelihood estimators of the unknown parameters of the NWM, as well as the associated Cramér-Rao (CR) bounds. We finally derive a CR-like bound on the global mean squared error (MSE) of the estimated nonlinearity of a NWM. Numerical results obtained for a pulse wave input are presented and compared to the ones based on the Nadaraya-Watson kernel estimator.
BibTeX
@inproceedings{icassp2019_onnonparametrici,
title = {On Nonparametric Identification of Wiener Systems with Deterministic Inputs},
author = {Simone Urbano and Eric Chaumette and Philippe Goupil and Jean-Yves Tourneret},
booktitle = {ICASSP 2019},
year = {2019}
}