Aeroengine Fault Diagnosis Using Optimized Elman Neural Network
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-12-19
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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 Vector Machine (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; QAPSO-ENN has great diagnostic ability in small samples.
American Psychological Association (APA)
Pi, Jun& Huang, Jiangbo& Ma, Long. 2017. Aeroengine Fault Diagnosis Using Optimized Elman Neural Network. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1192784
Modern Language Association (MLA)
Pi, Jun…[et al.]. Aeroengine Fault Diagnosis Using Optimized Elman Neural Network. Mathematical Problems in Engineering No. 2017 (2017), pp.1-8.
https://search.emarefa.net/detail/BIM-1192784
American Medical Association (AMA)
Pi, Jun& Huang, Jiangbo& Ma, Long. Aeroengine Fault Diagnosis Using Optimized Elman Neural Network. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1192784
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1192784