| SVR在混沌时间序列预测中的应用 |
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| 引用本文： | 孙德山,吴今培,肖健华.SVR在混沌时间序列预测中的应用[J].系统仿真学报,2004,16(3):519-520,524. |
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| 作者姓名： | 孙德山 吴今培 肖健华 |
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| 作者单位： | 1. 中南大学数学科学与计算技术学院,长沙,410075;辽宁师范大学数学系,大连,116029
2. 五邑大学智能技术与系统研究所,江门,529020 |
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| 基金项目： | 广东省自然科学基金(021349) |
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| 摘 要： | 支持向量机是一种基于统计学习理论的新颖的机器学习方法，该方法已广泛用于解决分类和回归问题。将支持向量回归算法应用于混沌时间序列预测中，并同BP网络及RBF网络的预测结果进行了比较分析。仿真实验表明，支持向量回归方法具有很好的泛化能力和一定的噪声容忍能力。
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| 关 键 词： | 支持向量机 支持向量回归 混沌时间序列 核函数 |
| 文章编号： | 1004-731X(2004)03-0519-02 |
The Application of SVR to Prediction of Chaotic Time Series |
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| SUN De-shan,WU Jin-pei,XIAO Jian-hua.The Application of SVR to Prediction of Chaotic Time Series[J].Journal of System Simulation,2004,16(3):519-520,524. |
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| Authors: | SUN De-shan WU Jin-pei XIAO Jian-hua |
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| Institution: | SUN De-shan1,2,WU Jin-pei3,XIAO Jian-hua3 |
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| Abstract: | Support vector machines (SVM) are a kind of novel machine learning methods based on statistical learning theory, which has been developed to solve classification and regression problems. This paper applies support vector regression (SVR) to chaotic time series prediction, and compares the prediction results with BP network and RBF network. The results of simulation experiments show that SVR has a good generalization ability and capability of tolerating noise. |
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| Keywords: | support vector machines support vector regression chaotic time series kernel function |
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