基于核主元分析和最小二乘支持向量机的软测量建模

软测量技术是工业过程控制和分析的有力工具，它的核心问题是如何建立学习速度快且泛化性能优良的软测量模型。提出了一种基于棱主元分析（KPCA）和最小二乘支持向量机（LSSVM）的软测量建模方法，利用核主元分析提取软测量输入数据空间中的非线性主元，然后用最小二乘支持向量机进行建模，不但降低模型复杂性，而且提高了模型泛化能力。最后将上述方法用于PTA结晶过程的软测量建模，仿真结果表明：与SVM、PCA-SVM建模方法相比，该KPCA-LSSVM方法具有学习速度快、跟踪性能好、泛化能力强等优点，是一种有效的软测量建模方法。

Soft Sensor Modeling Based on KPCA and Least Square SVM

Soft sensor is necessary for industrial process control and analysis, and the core problem is how to construct appropriate model having fast convergence speed and good generalization performance. A kind of soft sensor method was proposed based on kernel principle component analysis (KPCA) and least square support vector machine (LSSVM). KPCA was applied to choose the nonlinear principal component of the model input data space, and LSSVM was applied to proceed regression modelling, which could not only reduce the complexity of calculation but could improve the generalization ability. The proposed KPCA-LSSVM was applied to predict the granularity of PTA. Simulation indicates that this method features high learning speed, good approximation and good generalization ability compared with SVM and PCA-SVM, and is proved to be an efficient modeling method.