基于最小二乘支持向量机的软测量建模

软测量技术在工业过程控制中得到了广泛的应用，对保证产品质量和安全生产有很重要的作用。软测量技术的核心问题是建立优良的软测量数学模型。支持向量机是近几年发展起来的机器学习的新方法，它较好地解决了小样本、非线性、高维数、局部极小点等实际问题。本文研究了基于最小二乘支持向量机的软测量建模方法，并用交叉验证的方法进行支持向量机参数选择。将基于最小二乘支持向量机的软测量模型应用于轻柴油凝固点的预估。结果表明最小二乘支持向量机是软测量建模的一种非常有效的方法。

Soft Sensor Modeling Based on Support Vector Machines

Soft sensor has been widely used in industrial process control. It makes an important role to improve the quality of product and assure safety in production. The core problem of soft sensor is to construct appropriate mathematic model. Support vector machine (SVM) is a novel machine learning method, which is powerful for the problem characterized by small sample, nonlinearity, high dimension and local minima, and has high generalization. In this paper, soft sensor modeling method based on Least Square SVM (LS SVM) is proposed, and cross validation method is used to select hyper-parameter of LS SVM model. Soft sensor model based on LS SVM is applied to predication of frozen point of light diesel oil. Effective result indicates that LS SVM is of potential application in soft sensor.