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

Maarten Schoukens, Christian Lyzell, Martin Enqvist

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

2 Citaten (Scopus)

Samenvatting

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.

Originele taal-2Engels
Titel11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, ALCOSP 2013
Plaats van productieAmsterdam
UitgeverijElsevier
Pagina's372-377
Aantal pagina's6
ISBN van geprinte versie9783902823373
DOI's
StatusGepubliceerd - 22 okt. 2013
Extern gepubliceerdJa
Evenement11th IFAC Workshop on Adaptation and Learning in Control and Signal Processing, ALCOSP 2013 - Caen, Frankrijk
Duur: 3 jul. 20135 jul. 2013
Congresnummer: 11

Publicatie series

NaamIFAC Proceedings Volumes
Nummer11
Volume46

Congres

Congres11th IFAC Workshop on Adaptation and Learning in Control and Signal Processing, ALCOSP 2013
Verkorte titelALCOSP 2013
Land/RegioFrankrijk
StadCaen
Periode3/07/135/07/13

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