Quasiconvexity analysis of the Hammerstein model

Mohammad Rasouli, David Westwick, William Rosehart

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

In this paper, the Hammerstein identification problem with correlated inputs is studied in a prediction error framework using separable least squares methods. Thus, the identification is recast as an optimization over the parameters used to describe the nonlinearity. A sufficient condition is derived that guarantees that the identification problem is quasiconvex with respect to the parameters that describe the nonlinearity. Simulations using both IID and correlated inputs are used to illustrate the result.

Original languageEnglish (US)
Pages (from-to)277-281
Number of pages5
JournalAutomatica
Volume50
Issue number1
DOIs
StatePublished - Jan 1 2014

Fingerprint

Identification (control systems)

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Rasouli, Mohammad ; Westwick, David ; Rosehart, William. / Quasiconvexity analysis of the Hammerstein model. In: Automatica. 2014 ; Vol. 50, No. 1. pp. 277-281.
@article{747d9e677be74709b9a31b24c2df4f00,
title = "Quasiconvexity analysis of the Hammerstein model",
abstract = "In this paper, the Hammerstein identification problem with correlated inputs is studied in a prediction error framework using separable least squares methods. Thus, the identification is recast as an optimization over the parameters used to describe the nonlinearity. A sufficient condition is derived that guarantees that the identification problem is quasiconvex with respect to the parameters that describe the nonlinearity. Simulations using both IID and correlated inputs are used to illustrate the result.",
author = "Mohammad Rasouli and David Westwick and William Rosehart",
year = "2014",
month = "1",
day = "1",
doi = "10.1016/j.automatica.2013.11.004",
language = "English (US)",
volume = "50",
pages = "277--281",
journal = "Automatica",
issn = "0005-1098",
publisher = "Elsevier Limited",
number = "1",

}

Quasiconvexity analysis of the Hammerstein model. / Rasouli, Mohammad; Westwick, David; Rosehart, William.

In: Automatica, Vol. 50, No. 1, 01.01.2014, p. 277-281.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Quasiconvexity analysis of the Hammerstein model

AU - Rasouli, Mohammad

AU - Westwick, David

AU - Rosehart, William

PY - 2014/1/1

Y1 - 2014/1/1

N2 - In this paper, the Hammerstein identification problem with correlated inputs is studied in a prediction error framework using separable least squares methods. Thus, the identification is recast as an optimization over the parameters used to describe the nonlinearity. A sufficient condition is derived that guarantees that the identification problem is quasiconvex with respect to the parameters that describe the nonlinearity. Simulations using both IID and correlated inputs are used to illustrate the result.

AB - In this paper, the Hammerstein identification problem with correlated inputs is studied in a prediction error framework using separable least squares methods. Thus, the identification is recast as an optimization over the parameters used to describe the nonlinearity. A sufficient condition is derived that guarantees that the identification problem is quasiconvex with respect to the parameters that describe the nonlinearity. Simulations using both IID and correlated inputs are used to illustrate the result.

UR - http://www.scopus.com/inward/record.url?scp=84893678239&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84893678239&partnerID=8YFLogxK

U2 - 10.1016/j.automatica.2013.11.004

DO - 10.1016/j.automatica.2013.11.004

M3 - Article

VL - 50

SP - 277

EP - 281

JO - Automatica

JF - Automatica

SN - 0005-1098

IS - 1

ER -