By Topic

Linear Estimation in an Unknown Quasi-Stationary Environment

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)

Linear estimation under a minimum mean-square-error criterion in a quasi-stationary environment is considered. A generalized form of the Widrow-Hoff algorithm is employed for the estimation. Performance is measured by the excess error over the minimum meansquare error. A Gaussian assumption is used to determine this performance and determine simple bounds. The transient solution for the algorithm is investigated and a convergence rate determined. These results are used to optimize the algorithm parameters and bound the performance as a function of the environmental rate of change. The Robbins-Monro algorithm for finding the root of a linear regression function suggests the use of fixed step size stochastic approximation algorithms to solve more general quasi-stationary estimation problems.

Published in:

IEEE Transactions on Systems, Man, and Cybernetics  (Volume:SMC-1 ,  Issue: 3 )