Kalman Filtering and Neural Networks: 23 (Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control)

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