By Topic

Extended Stochastic Gradient Algorithms for System Modeling Based on the Auxiliary Model

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)
Dongqing Wang ; College of Automation Engineering, Qingdao University, Qingdao, P.R. China 266071. ; Chuangye Luan

This paper considers identification problems for output-error moving average systems with colored noises. The basic idea is, by the auxiliary model identification principle, to replace the unknown noise-free outputs and unmeasurable noise terms in the information vector with the outputs of an auxiliary model and the estimated residuals, and to present an auxiliary model based extended stochastic gradient algorithm. The algorithm proposed has significant computational advantage over existing least squares identification algorithms. The simulation example indicates that the parameter estimation errors become small as the data length increases.

Published in:

Networking, Sensing and Control, 2008. ICNSC 2008. IEEE International Conference on

Date of Conference:

6-8 April 2008