The Intelligent System design of Remote Fault Diagnosis of Reducer Based on GA and NN

被引：1

作者：

Su, Jun ^{[1
]}

机构：

[1] Nantong Univ, Dept Mech Engn, Nantong 226007, Jiangsu, Peoples R China

来源：

2009 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE, VOL III, PROCEEDINGS | 2009年

关键词：

component; NN; GA; weights; threshold; error curve;

D O I：

10.1109/AICI.2009.240

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Reducer failure was analyzed and by use of BP neural network in the paper.Model of failure diagnosis was established. By using genetic algorithms, the value of neural networks, the threshold, and the network structure were optimized. Genetic neural network model was applied to the system design of remote reducer fault diagnosis. To compare training error curve of BP neural network with genetic neural network, it was shown that genetic neural network in the training of speed and accuracy higher than the neural network training model.

引用

页码：36 / 39

页数：4

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