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 proceedingConference contributionAcademicpeer-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 languageEnglish
Title of host publication11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, ALCOSP 2013
Place of PublicationAmsterdam
PublisherElsevier
Pages372-377
Number of pages6
ISBN (Print)9783902823373
DOIs
Publication statusPublished - 22 Oct 2013
Externally publishedYes
Event11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing (ALCOSP 2013) - Caen, France
Duration: 3 Jul 20135 Jul 2013

Publication series

NameIFAC Proceedings Volumes
Number11
Volume46

Conference

Conference11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing (ALCOSP 2013)
Abbreviated titleALCOSP2013
CountryFrance
CityCaen
Period3/07/135/07/13

Keywords

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

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