基于小波分解和最小二乘支持向量机的COD预测
Prediction of COD Time Series Based on Wavelet and LS-SVM
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摘要: 膜生物反应器(MBR)的化学需氧量(COD),其值大小反映污水处理系统是否良好运行,对整个系统正常运行有重要意义.我们针对COD的非线性特征,引入基于小波分析和最小二乘向量机(LSSVM)的预测模型(WLSSVM).利用Mallat塔式分解算法获取趋势项与随机项,然后利用最小二乘支持向量机对时间序列分别预测,最后将各尺度下的分量整合作为预测值.试验结果表明,该模型具有较高的精度,是科学可行的.Abstract: MBR(Membrane Bio-Reactor)' s COD(chemical oxygen demand) have important effects on entire system.We design our wavelet transformation and LSSVM(Least Squares Support Vector Machine) method based on COD's nonlinear characteristic.First,we use Mallat deconstruction and reconstruction arithmetic to get trend and random time series,and then we use LSSVM to predict separately.Finally we combine every time series to get final result.Through simulation,our method show higher precision.
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