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Kalman Filtering and Neural Networks by Simon Haykin is a nonfiction available to read on EtoBox.

What is Kalman Filtering and Neural Networks about?

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 whi

Who reads Kalman Filtering and Neural Networks?

Author

Simon Haykin

Publisher

Wiley-Interscience

Published

2001

Language

EN

ISBN

9780471221548

Category

nonfiction

Subjects

Engineering, Mathematics, Science

Rating

4.33 / 5 (12 ratings)

Updated

2026-03-14