首页|期刊导航|高技术通讯（英文版）|A Novel Soft Sensor Modeling Approach Based on Least Squares Support Vector Machines
高技术通讯（英文版）2004，Vol.10Issue(4)：39-42,4.
A Novel Soft Sensor Modeling Approach Based on Least Squares Support Vector Machines
A Novel Soft Sensor Modeling Approach Based on Least Squares Support Vector Machines
摘要
Abstract
Artificial Neural Networks (ANNs) such as radial basis function neural networks (RBFNNs) have been successfully used in soft sensor modeling. However, the generalization ability of conventional ANNs is not very well. For this reason, we present a novel soft sensor modeling approach based on Support Vector Machines (SVMs). Since standard SVMs have the limitation of speed and size in training large data set, we hereby propose Least Squares Support Vector Machines (LS_SVMs) and apply it to soft sensor modeling. Systematic analysis is performed and the result indicates that the proposed method provides satisfactory performance with excellent approximation and generalization property. Monte Carlo simulations show that our soft sensor modeling approach achieves performance superior to the conventional method based on RBFNNs.关键词
support vector machines/least squares support vector machines/soft sensor/RBF neural networks/modelingKey words
support vector machines/least squares support vector machines/soft sensor/RBF neural networks/modeling分类
信息技术与安全科学引用本文复制引用
Feng Rui(冯瑞),Song Chunlin,Zhang Yanzhu,Shao Huihe..A Novel Soft Sensor Modeling Approach Based on Least Squares Support Vector Machines[J].高技术通讯（英文版）,2004,10(4):39-42,4.基金项目
Supported by the High Technology Research and Development Programme of China. （）
