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

DOI

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.

Copyright

© 2013, 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/).

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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  - Proceedings of the 2013 International Conference on Advanced Computer Science and Electronics Information (ICACSEI 2013)
PB  - Atlantis Press
SP  - 700
EP  - 703
SN  - 1951-6851
UR  - https://doi.org/10.2991/icacsei.2013.169
DO  - 10.2991/icacsei.2013.169
ID  - Dong2013/08
ER  -