DocumentCode
2748138
Title
An Improved BP Network Classifier Based on VPRS Feature Reduction
Author
Li, Mengxin ; Wu, Chengdong ; Zhang, Ying ; Yue, Yong
Author_Institution
Sch. of Inf. & Control Eng., Shenyang Jianzhu Univ.
Volume
2
fYear
0
fDate
0-0 0
Firstpage
9677
Lastpage
9680
Abstract
Variable precision rough sets (VPRS), as a extension of rough sets (RS) is adopted to reduce the redundant features for its ability of more useful information adopted compared with RS. The reduced features after VPRS are fed into the improved BP network proposed to inspect the defects of surface quality, which results in short training time and a high classification accuracy with a typical application in defect inspection of wood veneer
Keywords
backpropagation; computer vision; feature extraction; flaw detection; image classification; inspection; neural nets; rough set theory; backpropagation network classifier; defect inspection; image classification; redundant feature reduction; surface quality; variable precision rough sets; wood veneer; Computer networks; Control engineering; Data mining; Humans; Inspection; Production; Productivity; Rough sets; Rough surfaces; Surface roughness; An Improved BP Algorithm; Feature Reduction; VPRS; Wood Veneer;
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.2006.1713881
Filename
1713881
Link To Document