Kalman Filtering
Language: English
Published by John Wiley & Sons Okt 2001, 2001
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 201.19
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Add to basketItem description from seller
Neuware - State-of-the-art coverage of Kalman filter methods for the design of neural networksThis self-contained book consists of seven chapters by expert contributors that discuss Kalman filtering as applied to the training and use of neural networks. Although the traditional approach to the subject is almost always linear, this book recognizes and deals with the fact that real problems are most often nonlinear.The first chapter offers an introductory treatment of Kalman filters with an emphasis on basic Kalman filter theory, Rauch-Tung-Striebel smoother, and the extended Kalman filter. Other chapters cover:\* An algorithm for the training of feedforward and recurrent multilayered perceptrons, based on the decoupled extended Kalman filter (DEKF)\* Applications of the DEKF learning algorithm to the study of image sequences and the dynamic reconstruction of chaotic processes\* The dual estimation problem\* Stochastic nonlinear dynamics: the expectation-maximization (EM) algorithm and the extended Kalman smoothing (EKS) algorithm\* The unscented Kalman filterEach chapter, with the exception of the introduction, includes illustrative applications of the learning algorithms described here, some of which involve the use of simulated and real-life data. Kalman Filtering and Neural Networks serves as an expert resource for researchers in neural networks and nonlinear dynamical systems.; Die Kalman-Filterung ist ein wichtiges Spezialgebiet der Steuerungstechnik und Signalverarbeitung und die höchstentwickelte Methode für das Design neuronaler Netze. Der unkonventionelle, nichtlineare Ansatz trägt der Tatsache Rechnung, dass in der Praxis meist nichtlineare Probleme von Bedeutung sind. Besprochen werden wichtige Anwendungen, zum Beispiel aus der Steuerungstechnik und der Finanzmathematik.…
Seller Inventory # 9780471369981
- Title
- Kalman Filtering
- Author
- Haykin
- Publisher
- John Wiley & Sons Okt 2001
- Publication year
- 2001
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 0471369985
- ISBN 13
- 9780471369981
- Item weight
- 624 grams
- Dimensions
- 240x161x21 mm
- Series
- Book 7 of 33: Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control
This self-contained book consists of seven chapters by expert contributors that discuss Kalman filtering as applied to the training and use of neural networks. Although the traditional approach to the subject is almost always linear, this book recognizes and deals with the fact that real problems are most often nonlinear.
The first chapter offers an introductory treatment of Kalman filters with an emphasis on basic Kalman filter theory, Rauch-Tung-Striebel smoother, and the extended Kalman filter. Other chapters cover:
- An algorithm for the training of feedforward and recurrent multilayered perceptrons, based on the decoupled extended Kalman filter (DEKF)
- Applications of the DEKF learning algorithm to the study of image sequences and the dynamic reconstruction of chaotic processes
- The dual estimation problem
- Stochastic nonlinear dynamics: the expectation-maximization (EM) algorithm and the extended Kalman smoothing (EKS) algorithm
- The unscented Kalman filter
Each chapter, with the exception of the introduction, includes illustrative applications of the learning algorithms described here, some of which involve the use of simulated and real-life data. Kalman Filtering and Neural Networks serves as an expert resource for researchers in neural networks and nonlinear dynamical systems.
"Synopsis" may belong to another edition of this title.
About the Author
"About the title" may belong to another edition of this title.
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