Proceedings of the 2013 International Conference on Advanced Computer Science and Electronics Information (ICACSEI 2013)

Application of Radical Basis Function Neural Network in Fault Diagnosis of Rectifier

Authors
Jin zhao Dong, Yong cheng Xie, Jie yin Huang, Ying wen Zhai
Corresponding Author
Jin zhao Dong
Available Online August 2013.
DOI
https://doi.org/10.2991/icacsei.2013.169How to use a DOI?
Keywords
RBF neural network, Rectifier, Fault diagnosis.
Abstract
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.

Download article (PDF)

Proceedings
Part of series
Advances in Intelligent Systems Research
Publication Date
August 2013
ISBN
978-90-78677-74-1
ISSN
1951-6851
DOI
https://doi.org/10.2991/icacsei.2013.169How 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  - 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
PB  - Atlantis Press
SP  - 700
EP  - 703
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  -