Application of Radical Basis Function Neural Network in Fault Diagnosis of Rectifier
- Jin zhao Dong, Yong cheng Xie, Jie yin Huang, Ying wen Zhai
- Corresponding Author
- Jin zhao Dong
Available Online August 2013.
- https://doi.org/10.2991/icacsei.2013.169How to use a DOI?
- RBF neural network, Rectifier, Fault diagnosis.
- The Radical Basis Function (RBF) neural network is a kind of three-forward neural network, which can approximate any continuous functions to arbitrary precision, particularly suited to solve classification problems. In this paper, according armored vehicle power system silicon rectifier prophase fault is single diode and diode short-circuit fault in a very short period of time turned into the situation of the open-circuit fault, make full use of the characteristics of the RBF network classification to determine the fault to a special diode of the rectifier model. It achieves the aim at fault diagnosis of rectifier. Comparing with BP neural network, RBF neural network has better classification ability.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Jin zhao Dong AU - Yong cheng Xie AU - Jie yin Huang AU - Ying wen Zhai PY - 2013/08 DA - 2013/08 TI - Application of Radical Basis Function Neural Network in Fault Diagnosis of Rectifier BT - 2013 International Conference on Advanced Computer Science and Electronics Information (ICACSEI 2013) PB - Atlantis Press SN - 1951-6851 UR - https://doi.org/10.2991/icacsei.2013.169 DO - https://doi.org/10.2991/icacsei.2013.169 ID - Dong2013/08 ER -