Kalman Filtering and Neural Networks

Simon Haykin

ISBN 10: 0471369985 / ISBN 13: 9780471369981

Editore: Wiley Okt 2001, 2001

Riguardo questo articolo

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.

Codice articolo 9780471369981

Dati bibliografici

• Titolo: Kalman Filtering and Neural Networks
• Editore: Wiley Okt 2001
• Data di pubblicazione: 2001
• Lingua: Inglese
• ISBN 10: 0471369985
• ISBN 13: 9780471369981
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 240 L x 161 A x 21 P (millimetri) 240 millimetri di larghezza per 161 millimetri di altezza per 21 millimetri di profondità
• Peso: 624 grammi

Descrizione articolo

Riassunto

<b>State-of-the-art coverage of Kalman filter methods for the design of neural networks</b> <p>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.</p> <p>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:</p> <ul> <li>An algorithm for the training of feedforward and recurrent multilayered perceptrons, based on the decoupled extended Kalman filter (DEKF)</li> <li>Applications of the DEKF learning algorithm to the study of image sequences and the dynamic reconstruction of chaotic processes</li> <li>The dual estimation problem</li> <li>Stochastic nonlinear dynamics: the expectation-maximization (EM) algorithm and the extended Kalman smoothing (EKS) algorithm</li> <li>The unscented Kalman filter</li> </ul> <p>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.</p>

Informazioni sull'autore

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