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Kalman Filtering and Neural Networks, Hardcover by Haykin, Simon S. (EDT), ISBN 0471369985, ISBN-13 9780471369981, Brand New, Free P&P in the UK
Kalman filtering is discussed here as it is applied to the training and use of neural networks. 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. The first chapter offers an introductory treatment of Kalman filters, with an emphasis on basic Kalman filter theory, Rauchung-Striebel smoother, and the extended Kalman filter. Later chapters cover an algorithm for the training of feedforward and recurrent multilayered perceptrons, applications of the decoupled extended Kalman filter learning algorithm to the study of image sequences, the dual estimation problem, stochastic nonlinear dynamics, and the unscented Kalman filter. Each chapter includes applications of the learning algorithms described, using simulated and real-life data. Annotation c. Book News, Inc., Portland, OR ()
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