Cart (Loading....) | Create Account
Close category search window

An auxiliary model based multi-innovation recursive least squares estimation algorithms for MIMO Hammerstein system

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
$31 $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)
Wang Xiuping ; Wuxi Prof. Coll. of Sci. & Technol., Wuxi, China ; Chen Jing

An auxiliary model based multi-innovation recursive least squares estimation algorithms is proposed in this paper. The unknown variables in the information vector can be estimated by using the auxiliary model. The proposed recursive least squares algorithm uses not only the current innovation but also the past innovations at each recursion and thus the parameter estimation accuracy can be improved. Finally, the simulation results indicate that the proposed algorithm has good performances.

Published in:

Control Conference (CCC), 2011 30th Chinese

Date of Conference:

22-24 July 2011

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.