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Nonlinear state and parameter estimation of spatially distributed systems (Karlsruhe Series on Intelligent Sensor-Actuator-Systems, Universität Karlsruhe) - Softcover
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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.
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Nonlinear State and Parameter Estimation of Spatially Distributed Systems
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Karlsruher Institut f?r Technologie, 2014
ISBN 10: 3866443706
ISBN 13: 9783866443709
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Nonlinear State and Parameter Estimation of Spatially Distributed Systems
Published by
Karlsruher Institut f?r Technologie, 2014
ISBN 10: 3866443706
ISBN 13: 9783866443709
New
PAP
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Nonlinear state and parameter estimation of spatially distributed systems (Karlsruhe Series on Intelligent Sensor-Actuator-Systems, Universität Karlsruhe)
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KIT Scientific Publishing, 2009
ISBN 10: 3866443706
ISBN 13: 9783866443709
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Nonlinear state and parameter estimation of spatially distributed systems (Karlsruhe Series on Intelligent Sensor-Actuator-Systems, Universität Karlsruhe)
Published by
KIT Scientific Publishing, 2009
ISBN 10: 3866443706
ISBN 13: 9783866443709
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Nonlinear state and parameter estimation of spatially distributed systems (Karlsruhe Series on Intelligent Sensor-Actuator-Systems, Universität Karlsruhe)
Published by
KIT Scientific Publishing, 2009
ISBN 10: 3866443706
ISBN 13: 9783866443709
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Nonlinear state and parameter estimation of spatially distributed systems
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Karlsruher Institut f�r Technologie 2014-10, 2014
ISBN 10: 3866443706
ISBN 13: 9783866443709
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Nonlinear state and parameter estimation of spatially distributed systems
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Karlsruher Institut Für Technologie Mai 2009, 2009
ISBN 10: 3866443706
ISBN 13: 9783866443709
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Taschenbuch. Condition: 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. Seller Inventory # 9783866443709
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Nonlinear state and parameter estimation of spatially distributed systems
Published by
Karlsruher Institut Für Technologie, 2009
ISBN 10: 3866443706
ISBN 13: 9783866443709
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Taschenbuch. Condition: 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. Seller Inventory # 9783866443709
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Nonlinear state and parameter estimation of spatially distributed systems
Published by
KIT Scientific Publishing, 2009
ISBN 10: 3866443706
ISBN 13: 9783866443709
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Condition: 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. Seller Inventory # 5587808
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Nonlinear state and parameter estimation of spatially distributed systems
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Karlsruher Institut Für Technologie, Karlsruher Institut Für Technologie Okt 2014, 2014
ISBN 10: 3866443706
ISBN 13: 9783866443709
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Taschenbuch. Condition: Neu. 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. Seller Inventory # 9783866443709
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