Proceedings of the 2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016)

Extension Neural Network Optimized by Election Campaign Algorithm for Fault Diagnosis

Authors
Qinghua Xie, Xiangwei Zhang, Wenge Lv, Siyuan Cheng
Corresponding Author
Qinghua Xie
Available Online December 2016.
DOI
https://doi.org/10.2991/mcei-16.2016.153How to use a DOI?
Keywords
Fault diagnosis; Extension neural network; Matter element; Optimization; Election campaign algorithm
Abstract
Extension fault diagnosis is a new research direction in the field of intelligent fault diagnosis. The extension neural network model is introduced, including its structure and diagnostic principle. But for the problems of subjective parameters setting and algorithm precocious, the extension neural network model based on election campaign algorithm is proposed. It takes the dependent degree as the measurement and optimize the parameters by using election campaign algorithm. The results of experiment show that using this algorithm the entire fault can be correctly detected and the precision is high.
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Proceedings
2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016)
Part of series
Advances in Intelligent Systems Research
Publication Date
December 2016
ISBN
978-94-6252-282-4
ISSN
1951-6851
DOI
https://doi.org/10.2991/mcei-16.2016.153How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Qinghua Xie
AU  - Xiangwei Zhang
AU  - Wenge Lv
AU  - Siyuan Cheng
PY  - 2016/12
DA  - 2016/12
TI  - Extension Neural Network Optimized by Election Campaign Algorithm for Fault Diagnosis
BT  - 2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016)
PB  - Atlantis Press
SP  - 738
EP  - 742
SN  - 1951-6851
UR  - https://doi.org/10.2991/mcei-16.2016.153
DO  - https://doi.org/10.2991/mcei-16.2016.153
ID  - Xie2016/12
ER  -