Conference paper
Optimal experiment design for identification of grey-box models
Optimal experiment design is investigated for stochastic dynamic systems where the prior partial information about the system is given as a probability distribution function in the system parameters. The concept of information is related to entropy reduction in the system through Lindley's measure of average information, and the relationship between the choice of information related criteria and some estimators (MAP and MLE) is established.
A continuous time physical model of the heat dynamics of a building is considered and the results show that performing an optimal experiment corresponding to a MAP estimation results in a considerable reduction of the experimental length. Besides, it is established that the physical knowledge of the system enables us to design experiments, with the goal of maximizing information about the physical parameters of interest.
| Language: | English |
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| Publisher: | IEEE |
| Year: | 1994 |
| Pages: | 132-137 |
| Proceedings: | 1994 American Control Conference |
| ISBN: | 0780317831 and 9780780317833 |
| Types: | Conference paper |
| DOI: | 10.1109/ACC.1994.751709 |
| ORCIDs: | Madsen, Henrik |
Australia Bayesian methods Buildings Entropy MAP estimation Mathematical model Maximum likelihood estimation Probability distribution Random variables Statistical distributions Stochastic systems average information building heat dynamics continuous-time physical model grey-box model identification identification optimal experiment design optimisation prior partial information probability probability distribution function stochastic dynamic systems
