Name
Abstract | ||
|---|---|---|
A method is presented for estimating unknown parameters in distributed parameter systems. The system considered is assumed to be modeled by a stochastic partial differential equation whose form is known to be linear and unknown parameters are contained in exciting terms. Unknown parameters are assumed to be a set of random constants whose a priori probabilities are known. First, the estimation process of unknown parameters is given by the Bayesian approach in the Markovian framework. The dynamics of the state estimation is also given, which is simultaneously required in the parameter identification scheme. Secondly, the computing procedure is presented, circumventing tedious calculations of the covariance function between the system state and unknown parameters. Finally, two numerical examples are shown, emphasizing that the dynamics of the observation mechanisms adopted plays an important role in both the state estimation and parameter identification. |
Year | DOI | Venue |
|---|---|---|
1976 | 10.1016/0005-1098(76)90024-8 | Automatica |
Keywords | DocType | Volume |
distributed parameter system | Journal | 12 |
Issue | ISSN | Citations |
3 | 0005-1098 | 1 |
PageRank | References | Authors |
1.45 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Y Sunahara | 1 | 2 | 1.87 |
| A. Ohsumi | 2 | 6 | 3.19 |
| masaaki imamura | 3 | 1 | 1.45 |