基于SVM的软测量建模  

SOFT SENSOR MODELING BASED ON SUPPORT VECTOR MACHINE

作　　者：冯瑞[1] 张浩然[1] 邵惠鹤[1] 

机构地区：[1]上海交通大学自动化系,上海200030

出　　处：《信息与控制》2002年第6期567-571,共5页Information and Control

基　　金：国家"973"重点基础研究发展项目资助 (G19980 3 0 4)

摘　　要：支持向量机 (Support Vector Machines)是一种基于统计学习理论的新型学习机 ,本文提出用支持向量机建立软测量模型 .理论分析和仿真研究表明 ,该方法学习速度快、跟踪性能好、泛化能力强、对样本的依赖程度低 ,比基于Support vector machine (SVM) is a new learning machine based on the statistical learning theory. This paper presents a soft sensor model based on the SVM. Theoretical and simulation analysis indicates that this method features high learning speed, good approximation, well generalization ability, and little dependence on the sample set. It has the better performance than the soft sensor modeling based on the RBF neural network.

关 键 词：SVM 软测量 建模 支持向量机 RBF神经网络 工业过程控制 过程变量 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]