Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
393012 | Information Sciences | 2013 | 15 Pages |
Abstract
In this paper, the relationship between Gaussian noise and the loss function of the support vector regression machine (SVRM) is analyzed, and then a Gaussian loss function proposed to reduce the effect of such noise on the regression estimates. Since the ε-insensitive loss function cannot reduce noise, a novel support vector regression machine, g-SVRM, is proposed, then a chaotic particle swarm optimization (CPSO) algorithm developed to estimate its unknown parameters. Finally, a hybrid-forecasting model combining g-SVRM with the CPSO is proposed to forecast a multi-dimensional time series. The results of two experiments demonstrate the feasibility of this approach.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Qi Wu, Rob Law, Edmond Wu, Jinxing Lin,