动态加权最小二乘支持向量机

Dynamic Weighted Least Squares Support Vector Machines

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.

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.