基于多支持向量机的软测量模型

Soft Sensor Modeling Based on Multiple Support Vector Machines

在许多工业过程控制系统中,软测量技术由于解决了输出变量难以测量的问题而被广泛应用。软测量技术的核心问题是建立优良的软测量数学模型,支持向量机(SVM)以其优良的泛化特性而被应用到建立软测量模型中。基于多个模型的组合可以提高模型精度和鲁棒性的思想,提出多支持向量机(MSVM)组合模型的软测量建模方法。该建模方法通过减聚类方法将输入空间划分为一些小的局部空间,在每个局部空间中用最小二乘支持向量机(LS-SVM)建立子模型。为解决子模型相互之间的严重相关问题,提高模型的精度和鲁棒性,各个子模型的预测输出通过主元递归(PCR)方法连接。仿真研究表明,采用该建模方法能够达到较好的建模效果。

Soft sensor which solved the difficult problem of measuring the un-measurable output variables has been widely used in industrial process control. The core problem of soft sensor is to construct an appropriate mathematic model. Support vector machine （SVM） which has high generalization is adopted to establish soft sensor models. Based on the idea that the accuracy of model could be significantly improved by combining several sub-models, a multiple support vector machine （MSVM） modeling approach was proposed to build the soft sensor model. In this method, subtractive clustering was adopted to divide the input space into several sub-spaces, and sub-models were built by Least Square SVM （LS SVM） in every subspace. In order to minimize the severe correlation among sub-models, to improve the accuracy and robustness of the model the sub-models were combined by principal components regression （PCR）. The software sensor model accuracy is perfectly improved. The simulation results demonstrate the efficiency of the method.