| 动态加权最小二乘支持向量机 |
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| 引用本文： | 范玉刚, 李平,宋执环.动态加权最小二乘支持向量机[J].控制与决策,2006,21(10):1129-1133. |
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| 作者姓名： | 范玉刚 李平 宋执环 |
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| 作者单位： | 工业控制技术国家重点实验室,浙江大学,工业控制技术研究所,杭州,310027 |
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| 基金项目： | 国家863计划项目（2002AA4120L0-12）;浙江省科技计划项目（2004C31106）. |
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| 摘 要： | 提出一种基于动态加权最小二乘支持向量机（LS—SVM）的时间序列预测方法．动态加权LS—SVM能够跟踪时变非线性系统的动态特性，适合于系统辨识和时间序列预测；同时采用鲁棒方法确定权系数，以减小噪声的影响．将动态加权LS-SVM算法应用于工业PTA氧化过程中的4-CBA浓度预测，结果显示，动态加权LS—SVM预测精度高，能够有效减小噪声的影响．
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| 关 键 词： | 最小二乘支持向量机 时间序列预报 PTA氧化过程 |
| 文章编号： | 1001-0920（2006）10-1129-05 |
| 收稿时间： | 2005-08-15 |
| 修稿时间： | 2005-12-09 |
Dynamic Weighted Least Squares Support Vector Machines |
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| FAN Yu-gang,LI Ping,SONG Zhi-huan.Dynamic Weighted Least Squares Support Vector Machines[J].Control and Decision,2006,21(10):1129-1133. |
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| Authors: | FAN Yu-gang LI Ping SONG Zhi-huan |
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| Affiliation: | National Lab of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou 310027, China. |
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| Abstract: | A time series forecasting method based on dynamic weighted least squares support vector machine(LS-SVM) is proposed.Dynamic weighted LS-SVM is suitable for system recognition and time series prediction because the algorithm can track the dynamics of nonlinear time-varying systems.The weights are determined by a robust method in order to reduce the effect of the noise data.Dynamic weighted LS-SVM is applied to predict the concentration of 4-Carboxybenzaldchydc(4-CBA) in purified terephthalic acid(PTA) oxidation process.Results indicate that the proposed method reduces the effect of outliers and yields high accuracy. |
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| Keywords: | Least squares support vector Time series prediction PTA oxidation process |
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