Identification of Wiener Models in the Presence of Structural Disturbances

A4 Conference proceedings

Internal Authors/Editors

Publication Details

List of Authors: Amir H. Shirdel, Jari M. Böling, Hannu T. Toivonen
Editors: Denis Dochain, Didier Henrion, Dimitri Peaucelle
Publication year: 2017
Journal: IFAC papers online
Book title: 20th IFAC World Congress
Title of series: IFAC PapersOnLine
Number in series: 50
Volume number: 1
Start page: 14094
End page: 14099
ISSN: 2405-8963


In empirical system identification, non-stationary structural
disturbances, such as trends and outliers, can have a negative effect on
the estimation of the system parameters. As it not possible to
determine a priori which parts of the measured data stem from structural
disturbances and which are due to the system dynamics, the
identification of structural disturbances and system model should be
done simultaneously. In this study, a method for output error
identification of nonlinear Wiener models in the case when the
measurement is affected by trends and outliers is presented. The Wiener
model can be described by a dynamic linear block followed by an static
nonlinear block. In the proposed method the dynamic block is expanded
using orthonormal basis functions, while the static nonlinear block is
modeled by a kernel model. The kernel parameters and structural
disturbances are estimated simultaneously by using sparse optimization,
which is solved using l1-regularization and iterative reweighting. The feasibility of the proposed method is demonstrated on a simulated example.

Last updated on 2020-08-08 at 05:50