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.
出处
《信息与控制》
CSCD
北大核心
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)
