Aeroengine Fault Diagnosis Using Optimized Elman Neural Network

被引：3
|
作者：
Pi, Jun [1 ]
Huang, Jiangbo [1 ]
Ma, Long [1 ]
机构：
[1] Civil Aviat Univ China, Sinoeuropean Inst Aviat Engn, Tianjin 300300, Peoples R China
来源：
MATHEMATICAL PROBLEMS IN ENGINEERING | 2017年 / 2017卷
基金：
中国国家自然科学基金;
关键词：
PARTICLE SWARM; PREDICTION; SYSTEM;
D O I：
10.1155/2017/9726529
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
A new Elman Neural Network (ENN) optimized by quantum-behaved adaptive particle swarm optimization (QAPSO) is introduced in this paper. According to the root mean square error, QAPSO is used to select the best weights and thresholds of the ENN in training samples. The optimized neural network is applied to aeroengine fault diagnosis and is compared with other optimized ENN, original ENN, BP, and Support VectorMachine (SVM) methods. The results show that the QAPSO-ENN is more accurate and reliable in the aeroengine fault diagnosis than the conventional neural network and other ENN methods; QAPSOENN has great diagnostic ability in small samples.
引用
收藏
页数：8
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