Classification Technology for Automatic Surface Defects Detection of Steel Strip Based on Improved BP Algorithm

Author

Peng Kaixiang ; Zhang Xuli

Author_Institution

Sch. of Inf. Eng., Univ. of Sci. & Technol. Beijing, Beijing, China

Volume

1

fYear

2009

fDate

14-16 Aug. 2009

Firstpage

110

Lastpage

114

Abstract

The quality detection of the cold-strip steel using artificial neural networks is studied. A simple Back-propagation (BP) algorithm based on error function was presented. It deals with the saturation areas that play a significant role in the slow convergence of standard BP algorithm. A modified error function was constructed to make the weight adjustment to avoid falling into the saturation areas. The simulation and experiment results show the effect of improved BP algorithm on the classification of the surface defects of steel strip.

Keywords

backpropagation; convergence; neural nets; pattern classification; quality assurance; steel; steel industry; BP algorithm; artificial neural networks; automatic surface defects detection; backpropagation algorithm; classification technology; cold-strip steel; error function; quality detection; steel strip; Artificial neural networks; Backpropagation algorithms; Computer errors; Computer networks; Convergence; Inspection; Metals industry; Neural networks; Steel; Strips; Back-propagation; Cold-strip steel; Error function; Fast convergence; Surface defect;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation, 2009. ICNC '09. Fifth International Conference on

Conference_Location

Tianjin

Print_ISBN

978-0-7695-3736-8

Type

conf

DOI

10.1109/ICNC.2009.487

Filename

5363019