摘要
支持向量机是一种基于统计学习理论的新的机器学习方法,该方法已用于解决模式分类问题.本文将支持向量机(SVM)用于混沌时间序列分析,实验数据采用典型地Mackey-Glass混沌时间序列,先对混沌时间序列进行支持向量回归实验;然后采用局域法多步预报模型,利用支持向量机对混沌时间序列进行预测.仿真实验表明,利用支持向量机可以较准确地预测混沌时间序列的变化趋势.
Support vector machine （SVM） is a kind of novel machine learning methods based on statistical learning theory, which has been developed to slove pattern classification problems. This paper applied support vector machine to chaotic time series analysis. The experimental data was Mackey - Glass chaotic time series. First, the support vector regression method was used on the chaotic time series regression prolbem. Then, Local - Region Multi - steps Forecasting Model was used with supprot vector machine to predict the chaotic time sereis. Simulation results show that SVM could predict the trend of chaotic time series correctly.
出处
《动力学与控制学报》
2009年第1期5-8,共4页
Journal of Dynamics and Control
基金
国家杰出青年科学基金资助项目(50625518)~~
关键词
时间序列分析
混沌
支持向量机
time series analysis, chaos, support vector machine
