Identification of Wiener Models in the Presence of Structural Disturbances

A4 Konferenspublikationer

Interna författare/redaktörer

Publikationens författare: Amir H. Shirdel, Jari M. Böling, Hannu T. Toivonen
Redaktörer: Denis Dochain, Didier Henrion, Dimitri Peaucelle
Publiceringsår: 2017
Tidskrift: IFAC papers online
Moderpublikationens namn: 20th IFAC World Congress
Seriens namn: IFAC PapersOnLine
Nummer i serien: 50
Volym: 1
Artikelns första sida, sidnummer: 14094
Artikelns sista sida, sidnummer: 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.

Senast uppdaterad 2020-21-02 vid 05:09