SIMON HAYKIN – KALMAN FILTERING & NEURAL NETWORKS

State-of-the-art coverage of Kalman filter methods for the design of neural networks.

There are seven chapters in this self-contained book that discuss Kalman filters 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 not always linear.

Basic Kalman filter theory, Rauch-Tung-Striebel smoother, and the extended Kalman filter are some of the topics covered in the first chapter. There are other chapters that cover.

The decoupled extended Kalman filter is used for the training of feed forward and recurrent perceptrons.

The study of image sequence and the reconstruction of chaotic processes are some of the applications of the DEKF learning algorithm.

There is a dual estimation problem.

The Kalman smoothing and the expectation-maximization are two of the Stochastic nonlinear dynamics.

The Kalman filter has no scent.

Some of the illustrative applications of the learning algorithms described here involve the use of simulation and real-life data. Kalman Filtering and Neural Networks is an expert resource for researchers.

An Instructor’s Manual presenting detailed solutions to all the problems in the book can be requested from the Wiley Makerting Department.

Simon Haykin is a Kalman Filtering and Neural Networks expert.

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