By Topic

Study of an Improved Online Least Squares Support Vector Machine Algorithm and Its Application 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
$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)
Xiao-hu Zhao ; Sch. of Commun. & Electron. Eng., China Univ. of Min. & Technol., Xuzhou ; Ke-ke Zhao

The paper studied on gas prediction problem. According to traditional prediction methods on coal mine safety being offline without dynamic prediction function and the shortcomings in the traditional online learning with least squares support vector machine (LS-SVM), this paper gave an improved online prediction algorithm of LS-SVM. This algorithm was used in gas prediction of some coal mine. By comparing with the actual data and other relative algorithms, the paper proved effect of the algorithm.

Published in:

2008 Fourth International Conference on Natural Computation  (Volume:3 )

Date of Conference:

18-20 Oct. 2008