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OverviewState-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. Full Product DetailsAuthor: Simon Haykin (Communications Research Laboratory, McMaster University, Hamilton, Ontario, Canada)Publisher: John Wiley & Sons Inc Imprint: Wiley-Interscience Dimensions: Width: 16.20cm , Height: 1.90cm , Length: 24.60cm Weight: 0.544kg ISBN: 9780471369981ISBN 10: 0471369985 Pages: 304 Publication Date: 12 October 2001 Audience: College/higher education , Professional and scholarly , Undergraduate , Postgraduate, Research & Scholarly Format: Hardback Publisher's Status: Active Availability: Out of stock  The supplier is temporarily out of stock of this item. It will be ordered for you on backorder and shipped when it becomes available. Table of ContentsReviewsAlthough 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) 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) Author InformationSIMON HAYKIN, PhD, is Professor of Electrical Engineering at the Communication Research Laboratory of McMaster University in Hamilton, Ontario, Canada. Tab Content 6Author Website:Countries AvailableAll regions |
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