Kalman Filtering and Neural Networks (Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control) - Hardcover

Buch 7 von 33: Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control

Haykin, Simon

 
9780471369981: Kalman Filtering and Neural Networks (Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control)
Hardcover
ISBN 10: 0471369985 ISBN 13: 9780471369981
Verlag: Wiley-Interscience, 2001

Inhaltsangabe

State-of-the-art coverage of Kalman filter methods for the design of neural networks

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.

Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.

Über die Autorin bzw. den Autor

SIMON HAYKIN, PhD, is Professor of Electrical Engineering at the Communication Research Laboratory of McMaster University in Hamilton, Ontario, Canada.

Von der hinteren Coverseite

State-of-the-art coverage of Kalman filter methods for the design of neural networks

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.

Aus dem Klappentext

State-of-the-art coverage of Kalman filter methods for the design of neural networks

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.

„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.

Verlag
Wiley-Interscience
Erscheinungsdatum
2001
Sprache
Englisch
ISBN 10 
0471369985
ISBN 13 
9780471369981
Einband
Gebundene Ausgabe
Auflage
1
Anzahl der Seiten
304
Kontakt zum Hersteller
Wiley-VCH GmbH
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