RETRACTED: The Diagnosis of Tool Wear Based on RBF Neural Networks and D-S Evidence Theory (Retracted Article)

被引：0
|
作者：
Cao, Weiqing [1 ]
Fu, Pan [1 ]
Li, Weilin [1 ]
机构：
[1] Southwest Jiaotong Univ, Sch Mech Engn, Chengdu, Peoples R China
来源：
PROCEEDINGS OF 2010 3RD IEEE INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND INFORMATION TECHNOLOGY (ICCSIT 2010), VOL 7 | 2010年
关键词：
wear diagnosis; RBF neural network; D-S evidence theory;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In view of uncertain factors in the machining process, the paper puts forward a two-level information fusion method based on RBF neural network and D-S evidence theory. Three different signals were used to train and test three RBF neural networks and the outputs of three RBF networks were aggregated using the D-S evidence theory. Experiments show that the combination of RBF neural network and D-S evidence theory can improve the efficiency and accuracy of the tool wear fault diagnosis.
引用
收藏
页码：409 / 411
页数：3
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