Produktdetaljer
Biografisk notat
SIMON HAYKIN, PhD, is Professor of Electrical Engineering at the Communication Research Laboratory of McMaster University in Hamilton, Ontario, Canada."Although the traditional approach to the subject is usually linear, this book recognizes and deals with the fact that real problems are most often nonlinear." (<i>SciTech Book News</i>, Vol. 25, No. 4, December 2001)
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.
Contributors.
Kalman Filters (S. Haykin).
Parameter-Based Kalman Filter Training: Theory and Implementaion (G. Puskorius and L. Feldkamp).
Learning Shape and Motion from Image Sequences (G. Patel, et al.).
Chaotic Dynamics (G. Patel and S. Haykin).
Dual Extended Kalman Filter Methods (E. Wan and A. Nelson).
Learning Nonlinear Dynamical System Using the Expectation-Maximization Algorithm (S. Roweis and Z. Ghahramani).
The Unscencted Kalman Filter (E. Wan and R. van der Merwe).
Index.
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.