Automatic visual inspection and classification based on rough sets and neural network

被引：2
|
作者：
Li, MX [1 ]
Wu, CD [1 ]
Yue, Y [1 ]
机构：
[1] Shenyang Univ Architecture & Civil Engn, Shenyang 110015, Peoples R China
来源：
2003 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-5, PROCEEDINGS | 2003年
关键词：
rough sets; neural network; visual inspection; classification; defect;
D O I：
10.1109/ICMLC.2003.1260110
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a novel visual inspection and classification technology based on rough sets and neural network algorithm is presented. The rough set algorithm of data classification is discussed. As a large quantity of ambiguous and redundant data can be removed effectively using rough set theory, training time of neural networks is further decreased and the classification accuracy is also improved. Combined with anti-disturbance of the neural network, the effectiveness of classification technology is performed for the defect inspection of wood veneer with its rapid classification capacity and high classification accuracy.
引用
收藏
页码：3095 / 3099
页数：5
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