Application and research of the train fault diagnosis based on improved BP neural network algorithm

被引：2

作者：

Qu, Yingwei ^{[1
]}

Yan, Yinnan ^{[1
]}

Zheng, Guanghai ^{[1
]}

机构：

[1] Dalian Jiaotong Univ, Software Technol Inst, Dalian, Peoples R China

来源：

2012 INTERNATIONAL CONFERENCE ON CONTROL ENGINEERING AND COMMUNICATION TECHNOLOGY (ICCECT 2012) | 2012年

基金：

中国国家自然科学基金;

关键词：

Neural network; Fault diagnosis; step length; global optimal solution;

D O I：

10.1109/ICCECT.2012.26

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Traditional BP model of neural network is easy to get a local minimum rather than the global optimal solution. As the training times increases, the learning efficiency is falling low, so as the convergence rate. Improvement on the traditional model of BP neural network algorithm improves the convergence rate of the neural network, and reduces the training times, so that the output of the neural network can not only determine the type of the train failure occurred, to improve the accuracy of diagnostic results, but also to diagnose within a certain range even the fault does not appear, to make the fault of train intelligent and simple. The simulation results show that the improved algorithm is effective.

引用

页码：43 / 47

页数：5