Predicting Chaotic Time Series Using Relevance Vector Machine Regression

被引：0

作者：

Ye Meiying ^{[1
]}

Song Lina ^{[1
]}

Xu Yousheng ^{[1
]}

机构：

[1] Zhejiang Normal Univ, Dept Phys, Jinhua 321004, Peoples R China

来源：

PROCEEDINGS OF THE 31ST CHINESE CONTROL CONFERENCE | 2012年

关键词：

chaos; time series; prediction; relevance vector machine regression;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A chaotic time series prediction method based on relevance vector machine regression (RVMR) is proposed in this paper. The RVMR model has a simpler model structure, a fewer number of control parameters, and a faster prediction speed with comparable approximate prediction accuracy in comparison with support vector machine regression (SVMR). In addition, the kernel function must not necessarily fulfill Mercer's conditions in RVMR model. Two typical chaotic time series, namely, Logistic and Henon map are used to evaluate the RVMR's performance. The results show that the proposed method is effective in chaotic time series prediction.

引用

页码：2029 / 2033

页数：5