RETRACTED: The Diagnosis of Tool Wear Based on RBF Neural Networks and D-S Evidence Theory (Retracted Article)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

页码：409 / 411

页数：3

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