Chaotic Time Series Prediction Using Symmetric LS-SVM Regression

摘要：

In this article, we illustrate the effect of imposing symmetry as prior knowledge into the modeling stage within the context of chaotic time series predictions. It is illustrated that using Least-Squares Support Vector Machines with symmetry constraints improves the predicting performance for the cases of time series generated from the Lorenz equation.

关键词： Chaotic time series LS-SVM Symmetry constraints

作者: CUI Wanzhao CHEN Jia ZHENG Enrang

作者单位: National Key Laboratory of Space Microwave Technology, China Academy of Space Technology(Xian),7101 Shaanxi University of Science & Technology, Xian 710021, China

会议类型: 国际会议

会议名称: 第八届国际测试技术研讨会(8th International Symposium on Test and Measurement)

会议地点: 重庆

会议语种:英文

页码: 1659-1662

在线出版日期: 2009-08-01（万方平台首次上网日期，不代表论文的发表时间）