Haykin S. Kalman Filtering and Neural Networks

2001. – 284 p.
This self-contained book, consisting of seven chaplers. is devoted lo Kalman filter theory applied to the training and use of neural networks, and some applications of learning algorithms derived in this way. It is organized as follows:
Chapter 1 presents an inlroductory treatment of Kalman filters, with emphasis on basic Kalman filter theory, the Rauch-Tung-Striebel smoother, and the extended Kalman filter.
Chapter 2 presents the theoretical basis of a powerful learning algorithm for the training of feedforward and recurrent multilayered perceptions, based on the decoupled extended Kalman filter (DEKF); the theory presented here also includes a novel technique called multistreaming.
Chapters 3 and 4 present applications of the DEKF learning algorithm to the study of image sequences and the dynamic reconstruction of chaotic processes, respectively.
Chapter 5 studies the dual estimation problem, which refers to the problem of simultaneously estimating the state of a nonlinear dynamical system and the model that gives rise to die underlying dynamics of the system.
Chapter 6 studies how to learn stochastic nonlinear dynamics. This difficult learning task is solved in an elegant manner by combining two algorithms
Chapter 7 studies yet another novel idea-the unscented Kalman filter-the performance of which is superior to that of the extended Kalman filter