A maximum likelihood parameter estimation method for nonlinear dynamical systems

Author

David, B. ; Bastin, G.

Author_Institution

Center for Syst. Eng. & Appl. Mech., Univ. Catholique de Louvain, Belgium

Volume

1

fYear

1999

fDate

6/21/1905 12:00:00 AM

Firstpage

612

Abstract

Presents an original method for maximum likelihood parameter estimation in nonlinear dynamical systems with highly correlated residuals. The method relies on an autoregressive representation of the residuals to build an estimate of the inverse of its covariance matrix. Theoretical concepts are developed and we provide a successful application of the method on a two-parameters estimation problem with data collected on a real plant. This experimental study shows that the statistical properties of the estimated parameters are significantly improved with our method in comparison to classical estimation techniques that usually rely on an uncorrelated representation of the residuals. In addition, a far better estimation of the confidence region around the parameter vector is obtained

Keywords

Monte Carlo methods; covariance matrices; maximum likelihood estimation; nonlinear dynamical systems; probability; state-space methods; autoregressive representation; confidence region; highly correlated residuals; maximum likelihood parameter estimation method; two-parameters estimation problem; Cost function; Least squares methods; Maximum likelihood estimation; Nonlinear dynamical systems; Nonlinear systems; Parameter estimation; State estimation; Stochastic processes; Systems engineering and theory; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1999. Proceedings of the 38th IEEE Conference on

ISSN

0191-2216

Print_ISBN

0-7803-5250-5

Type

conf

DOI

10.1109/CDC.1999.832852

Filename

832852