Combining the best linear approximation and dimension reduction to identify the linear blocks of parallel Wiener systems

Maarten Schoukens; Christian Lyzell; Martin Enqvist

doi:10.3182/20130703-3-FR-4038.00026

Combining the best linear approximation and dimension reduction to identify the linear blocks of parallel Wiener systems

Maarten Schoukens, Christian Lyzell, Martin Enqvist

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

2 Citations (Scopus)

Abstract

A Wiener model is a fairly simple, well known, and often used nonlinear blockoriented black-box model. A possible generalization of the class of Wiener models lies in the parallel Wiener model class. This paper presents a method to estimate the linear time-invariant blocks of such parallel Wiener models from input/output data only. The proposed estimation method combines the knowledge obtained by estimating the best linear approximation of a nonlinear system with the MAVE dimension reduction method to estimate the linear timeinvariant blocks present in the model. The estimation of the static nonlinearity boils down to a standard static nonlinearity estimation problem starting from input-output data once the linear blocks are known.

Original language	English
Title of host publication	11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, ALCOSP 2013
Place of Publication	Amsterdam
Publisher	Elsevier
Pages	372-377
Number of pages	6
ISBN (Print)	9783902823373
DOIs	https://doi.org/10.3182/20130703-3-FR-4038.00026
Publication status	Published - 22 Oct 2013
Externally published	Yes
Event	11th IFAC Workshop on Adaptation and Learning in Control and Signal Processing, ALCOSP 2013 - Caen, France Duration: 3 Jul 2013 → 5 Jul 2013 Conference number: 11

Publication series

Name	IFAC Proceedings Volumes
Number	11
Volume	46

Conference

Conference	11th IFAC Workshop on Adaptation and Learning in Control and Signal Processing, ALCOSP 2013
Abbreviated title	ALCOSP 2013
Country/Territory	France
City	Caen
Period	3/07/13 → 5/07/13

Keywords

Best Linear Approximation
Dimension reduction
Dynamic Models
Nonlinear Models
Parallel Wiener
System identification

Access to Document

10.3182/20130703-3-FR-4038.00026

Cite this

Schoukens, M., Lyzell, C., & Enqvist, M. (2013). Combining the best linear approximation and dimension reduction to identify the linear blocks of parallel Wiener systems. In 11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, ALCOSP 2013 (pp. 372-377). (IFAC Proceedings Volumes; Vol. 46, No. 11). Elsevier. https://doi.org/10.3182/20130703-3-FR-4038.00026

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abstract = "A Wiener model is a fairly simple, well known, and often used nonlinear blockoriented black-box model. A possible generalization of the class of Wiener models lies in the parallel Wiener model class. This paper presents a method to estimate the linear time-invariant blocks of such parallel Wiener models from input/output data only. The proposed estimation method combines the knowledge obtained by estimating the best linear approximation of a nonlinear system with the MAVE dimension reduction method to estimate the linear timeinvariant blocks present in the model. The estimation of the static nonlinearity boils down to a standard static nonlinearity estimation problem starting from input-output data once the linear blocks are known.",

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Schoukens, M, Lyzell, C & Enqvist, M 2013, Combining the best linear approximation and dimension reduction to identify the linear blocks of parallel Wiener systems. in 11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, ALCOSP 2013. IFAC Proceedings Volumes, no. 11, vol. 46, Elsevier, Amsterdam, pp. 372-377, 11th IFAC Workshop on Adaptation and Learning in Control and Signal Processing, ALCOSP 2013, Caen, France, 3/07/13. https://doi.org/10.3182/20130703-3-FR-4038.00026

Combining the best linear approximation and dimension reduction to identify the linear blocks of parallel Wiener systems. / Schoukens, Maarten; Lyzell, Christian; Enqvist, Martin.
11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, ALCOSP 2013. Amsterdam: Elsevier, 2013. p. 372-377 (IFAC Proceedings Volumes; Vol. 46, No. 11).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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N2 - A Wiener model is a fairly simple, well known, and often used nonlinear blockoriented black-box model. A possible generalization of the class of Wiener models lies in the parallel Wiener model class. This paper presents a method to estimate the linear time-invariant blocks of such parallel Wiener models from input/output data only. The proposed estimation method combines the knowledge obtained by estimating the best linear approximation of a nonlinear system with the MAVE dimension reduction method to estimate the linear timeinvariant blocks present in the model. The estimation of the static nonlinearity boils down to a standard static nonlinearity estimation problem starting from input-output data once the linear blocks are known.

AB - A Wiener model is a fairly simple, well known, and often used nonlinear blockoriented black-box model. A possible generalization of the class of Wiener models lies in the parallel Wiener model class. This paper presents a method to estimate the linear time-invariant blocks of such parallel Wiener models from input/output data only. The proposed estimation method combines the knowledge obtained by estimating the best linear approximation of a nonlinear system with the MAVE dimension reduction method to estimate the linear timeinvariant blocks present in the model. The estimation of the static nonlinearity boils down to a standard static nonlinearity estimation problem starting from input-output data once the linear blocks are known.

KW - Best Linear Approximation

KW - Dimension reduction

KW - Dynamic Models

KW - Nonlinear Models

KW - Parallel Wiener

KW - System identification

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Schoukens M, Lyzell C, Enqvist M. Combining the best linear approximation and dimension reduction to identify the linear blocks of parallel Wiener systems. In 11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, ALCOSP 2013. Amsterdam: Elsevier. 2013. p. 372-377. (IFAC Proceedings Volumes; 11). doi: 10.3182/20130703-3-FR-4038.00026

Combining the best linear approximation and dimension reduction to identify the linear blocks of parallel Wiener systems

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