期刊
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
卷 23, 期 4, 页码 487-494出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2009.07.003
关键词
Support vector machine; Particle swarm optimization; Adaptive; Gaussian loss function; Forecasting
类别
资金
- National Natural Science Foundation of China [60904043]
- China Postdoctoral Science Foundation [20090451152]
- Jiangsu Planned Projects [0901023C]
- Shanghai Education Development Foundation [2008CG55]
In view of the bad capability of the standard support vector machine (SVM) in field of white noise of input series, a new v-SVM with Gaussian loss function which is call g-SVM is put forward to handle white noises. To seek the unknown parameters of g-SVM, an adaptive normal Gaussian particle swarm optimization (ANPSO) is also proposed. The results of applications show that the hybrid forecasting model based on the g-SVM and ANPSO is feasible and effective, the comparison between the method proposed in this paper and other ones is also given which proves this method is better than v-SVM and other traditional methods. (C) 2009 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据