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Beschreibung
In this thesis two probabilistic model-based estimators are introduced that allow the reconstruction and identification of space-time continuous physical systems. The Sliced Gaussian Mixture Filter (SGMF) exploits linear substructures in mixed linear/nonlinear systems, and thus is well-suited for identifying various model parameters. The Covariance Bounds Filter (CBF) allows the efficient estimation of widely distributed systems in a decentralized fashion.
Details
| Verlag | KIT Scientific Publishing |
| Ersterscheinung | 26. Mai 2009 |
| Maße | 21 cm x 14.8 cm |
| Gewicht | 300 Gramm |
| Format | Softcover |
| ISBN-13 | 9783866443709 |
| Seiten | 153 |
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