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Kalman filtering and neural networks / editör; Simon Haykin.

Contributor(s): Material type: TextTextLanguage: İngilizce Series: Adaptive and learning systems for signal processing, communications, and controlPublisher: New York : Wiley, 2002Copyright date: ©2001Description: 1 online resource (xiii, 284 pages) : illustrationsContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 047146421X
  • 9780471464211
  • 0471221546
  • 9780471221548
  • 0471369985
  • 9780471369981
Subject(s): Genre/Form: Additional physical formats: Print version:: Kalman filtering and neural networks.LOC classification:
  • QA76.87 .K35 2002EBK
Online resources:
Contents:
Kalman filters / Simon Haykin -- Parameter-based Kalman filter training : theory and implementation / Gintaras V. Puskorius and Lee A. Feldkamp -- Learning shape and motion from image sequences / Gaurav S. Patel, Sue Becker, and Ron Racine -- Chaotic Dynamics / Gaurav S. Patel and Simon Haykin -- Dual extended Kalman filter methods / Eric A. Wan and Alex T. Nelson -- Learning nonlinear dynamical systems using the expectation-maximization algorithm / Sam Roweis and Zoubin Ghahramani -- The unscented Kalman filter / Eric A. Wan and Rudolph van der Merwe.
Summary: 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.
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"A Wiley Interscience publication."

Includes index.

Kalman filters / Simon Haykin -- Parameter-based Kalman filter training : theory and implementation / Gintaras V. Puskorius and Lee A. Feldkamp -- Learning shape and motion from image sequences / Gaurav S. Patel, Sue Becker, and Ron Racine -- Chaotic Dynamics / Gaurav S. Patel and Simon Haykin -- Dual extended Kalman filter methods / Eric A. Wan and Alex T. Nelson -- Learning nonlinear dynamical systems using the expectation-maximization algorithm / Sam Roweis and Zoubin Ghahramani -- The unscented Kalman filter / Eric A. Wan and Rudolph van der Merwe.

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.

Print version record.

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