By Topic

A fast order-recursive algorithm for Toeplitz submatrix systems with applications to estimation of ARX systems

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

2 Author(s)
J. Pan ; Dept. of Electr. Eng., Maryland Univ., College Park, MD, USA ; W. S. Levine

The Levinson-type algorithms have not been applied to the linear minimum mean square error (LMMSE) estimation of parameters of an autoregressive system with exogenous inputs (ARX system) because the Yule-Walker equation in such a case is not a block-Toeplitz system, but is composed of block-Toeplitz submatrices. A new algorithm called the order-recursive algorithm (ORA) is developed to solve such systems, and it is applied to other LMMSE estimation problems of ARX systems. The resulting algorithm operates efficiently and recursively in the order of either the lagged output part or the exogenous input part. Meanwhile, it generates a set of LMMSE ARX models of different order as by-products. As a result, the ORA can be useful in many fields, including linear filtering of ARX and ARMA (autoregressive moving average) processes, system identification, model reduction, and adaptive control

Published in:

Decision and Control, 1990., Proceedings of the 29th IEEE Conference on

Date of Conference:

5-7 Dec 1990