Parameter identification of hammerstein models using elimination theory

Kaiyu Wang, Marc Bodson, John Chiasson, Leon M. Tolbert

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

A Hammerstein model is a system model in which the inputs go through a static nonlinearity followed by a linear time-invariant system. Often the static nonlinearity is modeled as a polynomial nonlinearity in the inputs or as a piecewise constant nonlinearity. Such models are nonlinear in the unknown parameters and therefore present a challenging identification problem. In this work, the authors show that elimination theory can be used to solve exactly for parameter values that minimize a least-square criterion. Thus, the approach guarantees the minimum can be found in a unite number of steps, unlike iterative methods that are currently used.

Original languageEnglish
Title of host publicationProceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05
Pages3444-3449
Number of pages6
DOIs
StatePublished - 2005
Event44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05 - Seville, Spain
Duration: 12 Dec 200515 Dec 2005

Publication series

NameProceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05
Volume2005

Conference

Conference44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05
Country/TerritorySpain
CitySeville
Period12/12/0515/12/05

Keywords

  • Hammerstein models
  • Nonlinear least-squares
  • Parameter identification
  • Resultants

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