9783866443709 - Nonlinear state and parameter estimation of spatially distributed systems (Karlsruhe Series on Intelligent Sensor-Actuator-Systems, Universität Karlsruhe) de Sawo, ... (6 resultados)

8° Paperback. Condición: Sehr gut. 153 S. 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. B05-04-03C Sprache: Englisch Gewicht in Gramm: 300.

Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 176 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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/nonlin.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 176 pp. Englisch.

Taschenbuch. Condición: Neu. Nonlinear state and parameter estimation of spatially distributed systems | Felix Sawo | Taschenbuch | 176 S. | Englisch | 2014 | Karlsruher Institut für Technologie | EAN 9783866443709 | Verantwortliche Person für die EU: Karlsruher Institut für Technologie (KIT), Institut AIFB, Kaiserstr. 89, 76133 Karlsruhe, verlag[at]aifb[dot]uni-karlsruhe[dot]de | Anbieter: preigu Print on Demand.