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Ajouter au panierEtat : New.
Edité par Springer New York 1994-01-01, 1994
ISBN 10 : 1461383943 ISBN 13 : 9781461383949
Langue: anglais
Vendeur : Chiron Media, Wallingford, Royaume-Uni
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Ajouter au panierPaperback. Etat : New.
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Ajouter au panierEtat : New. pp. 176.
Vendeur : HPB-Red, Dallas, TX, Etats-Unis
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Ajouter au panierHardcover. Etat : Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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Ajouter au panierEtat : New.
Edité par Springer-Verlag New York, Inc., 1994
ISBN 10 : 0387941983 ISBN 13 : 9780387941981
Langue: anglais
Vendeur : Antiquariat Bernhardt, Kassel, Allemagne
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Ajouter au paniergebundene Ausgabe. Etat : Sehr gut. Zust: Gutes Exemplar. XV, 156 Seiten, Englisch 474g.
Vendeur : online-buch-de, Dozwil, Suisse
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Ajouter au panierApr 28, 1994. Etat : gebraucht; wie neu.
Edité par Springer-Verlag, New York, 1994
ISBN 10 : 0387941983 ISBN 13 : 9780387941981
Langue: anglais
Vendeur : Chequamegon Books, Washburn, WI, Etats-Unis
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Ajouter au panierhardcover. Etat : fine. no jacket. 6 1/4 x 9 1/2 " 156 pages. written with the assistance of B. G. Williams. C. S. Burrus Consulting Editor. part of the Signal Processing and Digital Filtering series. "It discusses linear and nonlinear sequential filtering theory, that is, the problem of estimating a stochastic signal process indirectly observed through a sensor corrupted by a stochastic noise process.".
Edité par Springer, Chapman And Hall/CRC, 2011
ISBN 10 : 1461383943 ISBN 13 : 9781461383949
Langue: anglais
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Ajouter au panierTaschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - The theory of linear discrete time filtering started with a paper by Kol mogorov in 1941. He addressed the problem for stationary random se quences and introduced the idea of the innovations process, which is a useful tool for the more general problems considered here. The reader may object and note that Gauss discovered least squares much earlier; however, I want to distinguish between the problem of parameter estimation, the Gauss problem, and that of Kolmogorov estimation of a process. This sep aration is of more than academic interest as the least squares problem leads to the normal equations, which are numerically ill conditioned, while the process estimation problem in the linear case with appropriate assumptions leads to uniformly asymptotically stable equations for the estimator and the gain. The conditions relate to controlability and observability and will be detailed in this volume. In the present volume, we present a series of lectures on linear and nonlinear sequential filtering theory. The theory is due to Kalman for the linear colored observation noise problem; in the case of white observation noise it is the analog of the continuous-time Kalman-Bucy theory. The discrete time filtering theory requires only modest mathematical tools in counterpoint to the continuous time theory and is aimed at a senior-level undergraduate course. The present book, organized by lectures, is actually based on a course that meets once a week for three hours, with each meeting constituting a lecture.
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Ajouter au panierTaschenbuch. Etat : Neu. Lectures on Discrete Time Filtering | R. S. Bucy | Taschenbuch | xv | Englisch | 2011 | Springer | EAN 9781461383949 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Ajouter au panierEtat : Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.
Edité par Springer New York Nov 2011, 2011
ISBN 10 : 1461383943 ISBN 13 : 9781461383949
Langue: anglais
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Ajouter au panierTaschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The theory of linear discrete time filtering started with a paper by Kol mogorov in 1941. He addressed the problem for stationary random se quences and introduced the idea of the innovations process, which is a useful tool for the more general problems considered here. The reader may object and note that Gauss discovered least squares much earlier; however, I want to distinguish between the problem of parameter estimation, the Gauss problem, and that of Kolmogorov estimation of a process. This sep aration is of more than academic interest as the least squares problem leads to the normal equations, which are numerically ill conditioned, while the process estimation problem in the linear case with appropriate assumptions leads to uniformly asymptotically stable equations for the estimator and the gain. The conditions relate to controlability and observability and will be detailed in this volume. In the present volume, we present a series of lectures on linear and nonlinear sequential filtering theory. The theory is due to Kalman for the linear colored observation noise problem; in the case of white observation noise it is the analog of the continuous-time Kalman-Bucy theory. The discrete time filtering theory requires only modest mathematical tools in counterpoint to the continuous time theory and is aimed at a senior-level undergraduate course. The present book, organized by lectures, is actually based on a course that meets once a week for three hours, with each meeting constituting a lecture. 176 pp. Englisch.
Vendeur : Majestic Books, Hounslow, Royaume-Uni
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Ajouter au panierEtat : New. Print on Demand pp. 176 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam.
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Ajouter au panierEtat : New. PRINT ON DEMAND pp. 176.
Edité par Springer New York, Springer New York Nov 2011, 2011
ISBN 10 : 1461383943 ISBN 13 : 9781461383949
Langue: anglais
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Ajouter au panierTaschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -The theory of linear discrete time filtering started with a paper by Kol mogorov in 1941. He addressed the problem for stationary random se quences and introduced the idea of the innovations process, which is a useful tool for the more general problems considered here. The reader may object and note that Gauss discovered least squares much earlier; however, I want to distinguish between the problem of parameter estimation, the Gauss problem, and that of Kolmogorov estimation of a process. This sep aration is of more than academic interest as the least squares problem leads to the normal equations, which are numerically ill conditioned, while the process estimation problem in the linear case with appropriate assumptions leads to uniformly asymptotically stable equations for the estimator and the gain. The conditions relate to controlability and observability and will be detailed in this volume. In the present volume, we present a series of lectures on linear and nonlinear sequential filtering theory. The theory is due to Kalman for the linear colored observation noise problem; in the case of white observation noise it is the analog of the continuous-time Kalman-Bucy theory. The discrete time filtering theory requires only modest mathematical tools in counterpoint to the continuous time theory and is aimed at a senior-level undergraduate course. The present book, organized by lectures, is actually based on a course that meets once a week for three hours, with each meeting constituting a lecture.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 176 pp. Englisch.