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

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.
Proceedings of 49th IEEE Conference on Decision and Control
AUT
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/577314
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