基于PCA和最小二乘支持向量机的软测量建模

Soft Sensor Modeling Based on PCA and Support Vector Machines

软测量技术是解决工业过程中普遍存在的一类难以在线测量变量估计问题的有效方法,支持向量机是基于统计学习理论的一种新的机器学习方法。提出了一种基于主元分析(PCA)和最小二乘支持向量机的软测量建模方法,用主元分析对输入变量进行数据压缩,消除变量之间的相关性,简化支持向量机结构,并通过交叉验证的方法对支持向量机进行参数选择。将其用于4-CBA软测量建模的结果表明:该方法具有学习速度快、跟踪性能好以及泛化能力强等优点,为4-CBA软测量建模的在线实施提供了方便。

Soil sensor is an effective method to estimate variables which are difficult to be measured on-line in industrial processes. Support vector machine （SVM） is a novel machine learning method based on the statistical learning theory. A soft sensor based on principal component analysis （PCA） and Least Square SVM was proposed. The PCA method could not only solve the linear correlation of the input and compress data but also simply the SVM structure. Cross validation method was used to select parametrs of LS-SVM model. Soil sensor was applied to prediction of 4-CBA. Results indicates that this method features high learning speed, good approximation and well generalization ability. It provides convenice for on-line 4-CBA measurement.