Soft-sensing for Multiple models of Carbon Content of Fly Ash Based on SVM Fusion theory

被引：0

作者：

Pu Han ^{[1
]}

Hong Qiao ^{[2
]}

Yong-jie Zhai ^{[1
]}

Dong-feng Wang ^{[1
]}

机构：

[1] N China Elect Power Univ, Sch Control Sci & Engn, Baoding 071003, Peoples R China

[2] N china Elect Power Univ, Sch Energy & Power Engn, Beijing 102206, Peoples R China

来源：

2008 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-11 | 2008年

关键词：

Support vector machine; Multiple models; Carbon content of fly ash; Soft sensor;

D O I：

10.1109/CCDC.2008.4598200

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Because the accuracy of model could be significantly improved by combining multiple models, a support vectormachine (SVM) fusion modeling approach was proposed to build the soft sensor model. The model is built based on the time series data and the sub-model is built based on Least Square SVM algorithm in every sub-space. In order to minimize the severe correlation among sub-models, and improve the accuracy and robustness of the model, the sub-models are combined by SVM algorithm. In view of the problems that current power plant boiler ash carbon measurement methods are time-lag and low accuracy, we build a model using the method. The procedure of simulation and theoretical analysis indicate that the proposed method is effective.

引用

页码：4595 / +

页数：2