Kalman filtering and neural networks

Simon Haykin9780471369981, 9780471464211, 0471369985

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.

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Table of contents :
Kalman Filtering & Neural Networks……Page 1
Copyright……Page 4
Contents……Page 5
Preface……Page 11
Contributors……Page 13
Adaptive & Learning Systems for Signal Processing, Communications & Control……Page 15
Ch1 Kalman Filters……Page 16
Ch2 Parameter-Based Kalman Filter Training: Theory & Implementation……Page 37
Ch3 Learning Shape & motion from Image Sequences……Page 83
Ch4 Chaotic Dynamics……Page 97
Ch5 Dual Extended Kalman Filter Methods……Page 137
Ch6 Leaning Nonlinear Dynamical Systems using Expectation-Maximization Algorithm……Page 189
Ch7 Unscented Kalman Filter……Page 235
Index……Page 295