Discussion About Nonlinear Time Series Prediction Using Least Squares Support Vector Machine

被引：0
|
作者：
XU Rui-Rui
BIAN Guo-Xing
GAO Chen-Feng
CHEN Tian-Lun Department of Physics
机构：
来源：
CommunicationsinTheoreticalPhysics | 2005年 / 43卷 / 06期
基金：
中国国家自然科学基金;
关键词：
least squares support vector machine; nonlinear time series; prediction; clustering;
D O I：
暂无
中图分类号：
O411.1 [数学物理方法];
学科分类号：
0701 ; 070104 ;
摘要：
The least squares support vector machine (LS-SVM) is used to study the nonlinear time series prediction.First, the parameter γ and multi-step prediction capabilities of the LS-SVM network are discussed. Then we employclustering method in the model to prune the number of the support values. The learning rate and the capabilities offiltering noise for LS-SVM are all greatly improved.
引用
收藏
页码：1056 / 1060
页数：5
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