Chaotic Time Series Prediction Using Symmetric LS-SVM Regression
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.
Chaotic time series LS-SVM Symmetry constraints
CUI Wanzhao CHEN Jia ZHENG Enrang
National Key Laboratory of Space Microwave Technology, China Academy of Space Technology(Xian),7101 Shaanxi University of Science & Technology, Xian 710021, China
国际会议
第八届国际测试技术研讨会(8th International Symposium on Test and Measurement)
重庆
英文
1659-1662
2009-08-01(万方平台首次上网日期,不代表论文的发表时间)