Kalman Filtering and Neural Networks by Simon Haykin - 9780471369981

Kalman Filtering and Neural NetworksAuthor(s): Simon Haykin\nFormat: Hardback\nPublisher: John Wiley & Sons Inc, United States\nImprint: Wiley-Interscience\nISBN-13: 9780471369981, 978-0471369981\nSynopsis\nState-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:\nAn algorithm for the training of feedforward and recurrent multilayered perceptrons, based on the decoupled extended Kalman filter (DEKF) Applications of the DEKF learning alg.