Kalman Filtering and Neural Networks

Simon Haykin

ISBN 10: 0471369985 / ISBN 13: 9780471369981

Published by John Wiley & Sons Inc, 2001

About this Item

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. Series: Adaptive and Learning Systems for Signal Processing, Communications and Control Series. Num Pages: 304 pages, Ill. BIC Classification: TJFC; UYQN; UYS. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 245 x 166 x 23. Weight in Grams: 582. . 2001. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.

Seller Inventory # V9780471369981

Bibliographic Details

• Title: Kalman Filtering and Neural Networks
• Publisher: John Wiley & Sons Inc
• Publication Date: 2001
• Language: English
• ISBN 10: 0471369985
• ISBN 13: 9780471369981
• Binding: Hardcover
• Condition: New

About this title

Synopsis

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.

About the Author

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