Simon Haykin – Kalman Filtering & Neural Networks

Description

Simon Haykin – Kalman Filtering & Neural Networks

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Description:

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

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.

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