Title :
Approximate maximum likelihood parameter estimates for stochastic distributed parameter systems
Author_Institution :
Dept. of Electr. Eng., Alabama Univ., Tuscaloosa, AL, USA
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;
Conference_Titel :
American Control Conference, 1997. Proceedings of the 1997
Conference_Location :
Albuquerque, NM
Print_ISBN :
0-7803-3832-4
DOI :
10.1109/ACC.1997.609518
