A Novel Update Algorithm of Least Squares Support Vector Machine for Industrial Process Modeling

被引：0
|
作者：
Yang, Tingting [1 ]
Lv, You [2 ]
Chang, Taihua [1 ]
Gao, Jin [1 ]
机构：
[1] North China Elect Power Univ, Sch Control & Comp Sci Engn, Beijing 102206, Peoples R China
[2] North China Elect Power Univ, State Key Lab Alternate Elect Power Syst Renewabl, Beijing 102206, Peoples R China
来源：
11TH IEEE INTERNATIONAL CONFERENCE ON CONTROL AND AUTOMATION (ICCA) | 2014年
关键词：
BOILER;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
An update algorithm of least squares support vector machine (LSSVM) is proposed to tackle the time-varying characteristics of the real industrial process. The process variations are concluded to two categories, and accordingly the samples adding and samples replacement are proposed to update the initial LSSVM model incrementally. Then the LSSVM model with proposed updating measures is applied in the prediction of SO2 concentration in the sulfur recovery unit (SRU) process. The results reveal that the prediction accuracy of the model with update maintains high in spite of the process characteristics varying.
引用
收藏
页码：1287 / 1291
页数：5
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