Approximate maximum likelihood parameter estimates for stochastic distributed parameter systems

Author

Leland, Robert

Author_Institution

Dept. of Electr. Eng., Alabama Univ., Tuscaloosa, AL, USA

Volume

6

fYear

1997

fDate

4-6 Jun 1997

Firstpage

3693

Abstract

We describe a fast method to calculate maximum likelihood parameter estimates for stochastic distributed parameter systems from sampled data. We characterize the observations by an approximate one step predictor that uses a fixed number of past observations, and its error co-variance. From these we calculate the log-likelihood functional, and find the estimate by iterative search. Estimators for an integral equation and two heat equations driven by white noise are derived and simulated

Keywords

autoregressive processes; distributed parameter systems; integral equations; iterative methods; maximum likelihood estimation; prediction theory; search problems; stochastic systems; AR model; approximate one step predictor; distributed parameter systems; heat equations; integral equation; iterative search; maximum likelihood estimation; parameter estimation; stochastic systems; white noise; Distributed parameter systems; Integral equations; Least squares approximation; Maximum likelihood estimation; Parameter estimation; Predictive models; Riccati equations; Stochastic systems; Technological innovation; White noise;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 1997. Proceedings of the 1997

Conference_Location

Albuquerque, NM

ISSN

0743-1619

Print_ISBN

0-7803-3832-4

Type

conf

DOI

10.1109/ACC.1997.609518

Filename

609518