By Topic

Study of a new online Least Squares Support Vector Machine algorithm in gas prediction

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)
Xiao-hu Zhao ; Sch. of Commun. & Electron. Eng., China Univ. of Min. & Technol., Beijing ; Ke-ke Zhao

This paper studied on time series prediction, and proposes a new prediction algorithm of LS-SVM online learning against the shortcomings in the traditional online learning with least squares support vector machine. This algorithm was researched and used in coal mine gas prediction and had proved effective, compared with the actual data and other relative algorithms.

Published in:

Industrial Informatics, 2008. INDIN 2008. 6th IEEE International Conference on

Date of Conference:

13-16 July 2008