Extension Neural Network Optimized by Election Campaign Algorithm for Fault Diagnosis
Qinghua Xie, Xiangwei Zhang, Wenge Lv, Siyuan Cheng
Available Online December 2016.
- 10.2991/mcei-16.2016.153How to use a DOI?
- Fault diagnosis; Extension neural network; Matter element; Optimization; Election campaign algorithm
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.
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
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 - Proceedings of the 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 - 10.2991/mcei-16.2016.153 ID - Xie2016/12 ER -