Kalman Filtering and Neural Networks

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9780471369981: Kalman Filtering and Neural Networks

Kalman Filtering and Neural Networks Kalman filtering is a well-established topic in the field of control and signal processing and represents by far the most refined method for the design of neural networks. This book takes a nontraditional nonlinear approach and reflects the fact that most practical applications are nonlinear. Full description

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Présentation de l'éditeur :

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.

An Instructor′s Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley Makerting Department.

Revue de presse :

"Although the traditional approach to the subject is usually linear, this book recognizes and deals with the fact that real problems are most often nonlinear." ( SciTech Book News, Vol. 25, No. 4, December 2001)

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Simon S. Haykin
Edité par John Wiley Sons Inc, United States (2001)
ISBN 10 : 0471369985 ISBN 13 : 9780471369981
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Description du livre John Wiley Sons Inc, United States, 2001. Hardback. État : New. New.. 242 x 161 mm. Language: English . Brand New Book. 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. An Instructor s Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley Makerting Department. N° de réf. du libraire AAH9780471369981

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Description du livre John Wiley Sons Inc, United States, 2001. Hardback. État : New. New.. 242 x 161 mm. Language: English . Brand New Book. 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. An Instructor s Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley Makerting Department. N° de réf. du libraire AAH9780471369981

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Description du livre 2001. Hardcover. État : New. 0004th. 162mm x 20mm x 242mm. Hardcover. 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.Shipping may be from multiple locations in the US or from the UK, depending on stock availability. 304 pages. 0.544. N° de réf. du libraire 9780471369981

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