Study on Support Vector Machine in Calculating Steel Quenching Degree

Author

An, Wensen ; Sun, Yanguang ; Wang, Deji

Author_Institution

Dept. of Autom., Univ. of Sci. & Technol. of China

Volume

2

fYear

0

fDate

0-0 0

Firstpage

7780

Lastpage

7783

Abstract

The calculation of steel quenching degree has an important influence on real application. Steel quenching degree is influenced by chemical constitution and other many factors, which makes it difficult to be calculated accurately. Support vector machine (SVM) is a novel machine learning method based on statistical learning theory, which is powerful for solving the problems described by high dimension, small-sample and nonlinearity. In this paper, an SVM-based approach applied to calculate steel quenching degree is presented. With real data collected from Jiangyin Xingcheng Steel Work Co. Ltd., experiments show that SVM-based method is effective and superior to ANN-based method

Keywords

quenching (thermal); statistical analysis; steel; steel industry; steel manufacture; support vector machines; SVM-based approach; machine learning; statistical learning theory; steel quenching degree; support vector machine; Chemical technology; Constitution; Design automation; Learning systems; Metals industry; Risk management; Statistical learning; Steel; Sun; Support vector machines; calculation; steel quenching degree; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1713483

Filename

1713483