| 时序峰值预测的最小二乘支持向量回归模型 |
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| 引用本文： | 袁从贵,张新政. 时序峰值预测的最小二乘支持向量回归模型[J]. 控制与决策, 2012, 27(11): 1745-1750 |
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| 作者姓名： | 袁从贵 张新政 |
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| 作者单位： | 广东工业大学自动化学院;东莞职业技术学院电子工程系 |
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| 基金项目： | 国家自然科学基金项目(61074185);广东省中国科学院全面战略合作项目(2010B090301042);广州科技计划项目(2011J4300079) |
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| 摘 要： | 针对最小二乘支持向量回归模型中,呈稀疏分布的时序峰值样本拟合预测误差偏大的问题,基于加权最小二乘思想,提出一种新的用于时序峰值预测的最小二乘支持向量回归模型.根据样本分布密度和输出期望幅值,优化了经验风险控制目标.解得模型的拟合预测误差不受样本分布的影响,而且在保持整体样本拟合预测精度的同时,对峰值样本的拟合预测精度有了显著提高.Lorenz时序预测和电力负荷预测的仿真结果表明了模型的有效性.
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| 关 键 词： | 峰值预测 支持向量回归 加权最小二乘 核密度估计 |
| 收稿时间： | 2011-05-16 |
| 修稿时间： | 2011-08-31 |
Least Squares Support Vector Regression for Prediction of Peak Samples in Time Series |
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| YUAN Cong-gui,ZHANG Xin-zheng. Least Squares Support Vector Regression for Prediction of Peak Samples in Time Series[J]. Control and Decision, 2012, 27(11): 1745-1750 |
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| Authors: | YUAN Cong-gui ZHANG Xin-zheng |
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| Affiliation: | 1(1.School of Automation,Guangdong University of Technology,Guangzhou 511442,China;2.Department of Electronic Engineering,Dongguan Polytechnic College,Dongguan 523808,China.) |
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| Abstract: | The sparse distributed peak samples in time series are poorly fitted in the least squares support vector regression
model. Therefore, a new least squares support vector regression model is proposed based on weighted least squares method
and used to predict peak samples in time series. In this model, the structural risk objective is optimized by the distribution
density and the amplitude of expected output. The fitting errors of model are not influenced by the distribution of samples,
and the fitting and prediction accuracy of the peak samples is improved significantly with the holistic accuracy maintained
simultaneously. The simulation results on the Lorenz time series prediction and load prediction in power system show the
effectiveness of the model. |
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