Dynamic Support Vector Machine Regression Based on Recurrent Strategy

被引：0

作者：

Wang, Jing ^{[1
]}

Huang, Yinghua ^{[1
]}

Cao, Liulin ^{[1
]}

Jin, Qibing ^{[1
]}

机构：

[1] Beijing Univ Chem Technol, Automat Inst, Beijing 100029, Peoples R China

来源：

2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23 | 2008年

关键词：

SVR; DR-SVR; recursive algorithm; PET process;

D O I：

10.1109/WCICA.2008.4593430

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Currently, the research and application of support vector machines regression (SVR) mainly focus on the statistical modeling of the complex industrial process, which is difficult to meet the requirement of real dynamic process control. In order to overcome this shortcoming, a really dynamic recurrent support vector machine regression model (DR-SVR) is proposed, within which inner structure cells are introduced based on recurrent strategy. Then a recursive algorithm for input data with sequence supply rather than batch is given. Finally, the new DR-SVR method is used to identify the dynamic model of the PET process. The simulation results show that the presented DR-SVR model has better performance than the normal SVR in the identification of industrial process.

引用

页码：3177 / 3181

页数：5

共 6 条

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