| 基于SVM的软测量建模 |
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| 引用本文： | 冯瑞,张浩然,邵惠鹤.基于SVM的软测量建模[J].信息与控制,2002,31(6):567-571. |
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| 作者姓名： | 冯瑞 张浩然 邵惠鹤 |
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| 作者单位： | 上海交通大学自动化系,上海,200030 |
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| 基金项目： | 国家“973”重点基础研究发展项目资助 (G19980 3 0 4) |
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| 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RBF神经网络的软测量建模具有更好的
推广能力．
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| 关 键 词： | 支持向量机(SVMs) 软测量 RBF神经网络 建模 |
| 文章编号： | 1002-0411(2002)06-567-05 |
SOFT SENSOR MODELING BASED ON SUPPORT VECTOR MACHINE |
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| FENG Rui\ ZHANG Hao,ran\ SHAO Hui,he.SOFT SENSOR MODELING BASED ON SUPPORT VECTOR MACHINE[J].Information and Control,2002,31(6):567-571. |
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| Authors: | FENG Rui\ ZHANG Hao ran\ SHAO Hui he |
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| Abstract: | 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. |
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| Keywords: | support vector machine (SVMs) soft sensor RBF neural network modeling |
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