Description
Book SynopsisKalman filtering is a well-established topic in the field of control and signal processing and represents by far the most refined method for the design of neural networks. This book takes a nontraditional nonlinear approach and reflects the fact that most practical applications are nonlinear.
Trade Review"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." (
SciTech Book News, Vol. 25, No. 4, December 2001)
Table of ContentsPreface.
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.
