By Topic

Research for breakout prediction system based on support vector regression

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

3 Author(s)
Tian Qing ; Hebei United Univ., Tangshan, China ; Wang Jia-Wei ; Xue Ji-Shuang

SVM is widely used in the pattern recognition. It shows prediction ability well. For the system nonlinear and complexity of the CCM bonding breakout forecast system nonlinear, complexity, and breakout forecast system based on the least squares support vector machine (LSSVM) is put forward. In forecast system, establish 0-1 more value data window to eliminate the redundant data. The simulation results show that the LSSVM model cans quickly the training sample parameters in the small sample. It shows strong recognition ability, high precision.

Published in:

Robotics and Applications (ISRA), 2012 IEEE Symposium on

Date of Conference:

3-5 June 2012