This work extends to the nonlinear framework some previous results concerning the convergence of simulation error minimization (SEM) methods for parameter estimation based on an iterative predictor estimation with increasing prediction horizon. Conditions for the applicability of the approach to various model classes, including bilinear, Hammerstein, Wiener and LPV models, are also discussed. The effectiveness of the iterative predictor estimation approach is then shown by means of a simulation example.

Convergence properties of an iterative prediction approach to nonlinear SEM parameter estimation

FARINA, MARCELLO;PIRODDI, LUIGI
2010-01-01

Abstract

This work extends to the nonlinear framework some previous results concerning the convergence of simulation error minimization (SEM) methods for parameter estimation based on an iterative predictor estimation with increasing prediction horizon. Conditions for the applicability of the approach to various model classes, including bilinear, Hammerstein, Wiener and LPV models, are also discussed. The effectiveness of the iterative predictor estimation approach is then shown by means of a simulation example.
2010
Proceedings of 49th IEEE Conference on Decision and Control
AUT
File in questo prodotto:
File Dimensione Formato  
2010 - CDC - FarinaPiroddi.pdf

Accesso riservato

: Post-Print (DRAFT o Author’s Accepted Manuscript-AAM)
Dimensione 496.07 kB
Formato Adobe PDF
496.07 kB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/577314
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact