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**Harvard**

Sjöberg, J. och Schoukens, J. (2011) *Initializing Wiener-Hammerstein Models Based on Partitioning of the Best Linear Approximation*.

** BibTeX **

@conference{

Sjöberg2011,

author={Sjöberg, Jonas and Schoukens, Johan},

title={Initializing Wiener-Hammerstein Models Based on Partitioning of the Best Linear Approximation},

booktitle={IFAC Proceedings Volumes. 18th IFAC World Congress, Milano, 28 August - 2 September 2011},

isbn={978-3-902661-93-7},

pages={11177-11182},

abstract={This paper describes a new algorithm for initializing and estimating Wiener-
Hammerstein models. The algorithm makes use of the best linear model of the system which
is split in all possible ways into two linear sub-models. For all possible splits, a Wiener-
Hammerstein model is initialized which means that a nonlinearity is introduced in between
the two sub-models. The linear parameters of this nonlinearity can be estimated using leastsquares.
All initialized models can then be ranked with respect to their fit. Typically, one is only
interested in the best one, for which all parameters are fitted using prediction error minimization.
The paper explains the algorithm and the consistency of the initialization is stated. Computational
aspects are investigated, showing that in most realistic cases, the number of splits of
the initial linear model remains low enough to make the algorithm useful. The algorithm is
illustrated on an example where it is shown that the initialization is a tool to avoid many local
minima.},

year={2011},

keywords={Wiener-Hammerstein systems, Hammerstein systems, Wiener systems, nonlinear system identification},

}

** RefWorks **

RT Conference Proceedings

SR Electronic

ID 148068

A1 Sjöberg, Jonas

A1 Schoukens, Johan

T1 Initializing Wiener-Hammerstein Models Based on Partitioning of the Best Linear Approximation

YR 2011

T2 IFAC Proceedings Volumes. 18th IFAC World Congress, Milano, 28 August - 2 September 2011

SN 978-3-902661-93-7

SP 11177

OP 11182

AB This paper describes a new algorithm for initializing and estimating Wiener-
Hammerstein models. The algorithm makes use of the best linear model of the system which
is split in all possible ways into two linear sub-models. For all possible splits, a Wiener-
Hammerstein model is initialized which means that a nonlinearity is introduced in between
the two sub-models. The linear parameters of this nonlinearity can be estimated using leastsquares.
All initialized models can then be ranked with respect to their fit. Typically, one is only
interested in the best one, for which all parameters are fitted using prediction error minimization.
The paper explains the algorithm and the consistency of the initialization is stated. Computational
aspects are investigated, showing that in most realistic cases, the number of splits of
the initial linear model remains low enough to make the algorithm useful. The algorithm is
illustrated on an example where it is shown that the initialization is a tool to avoid many local
minima.

LA eng

DO 10.3182/20110828-6-IT-1002.00142

LK http://dx.doi.org/10.3182/20110828-6-IT-1002.00142

LK http://publications.lib.chalmers.se/records/fulltext/local_148068.pdf

OL 30