Automobile Fault Diagnosis System based on Improved Neural Network

被引：0

作者：

Gang, Hao ^{[1
]}

机构：

[1] Shijiazhuang Vocat Technol Inst, Shijiazhuang 050081, Hebei, Peoples R China

来源：

2016 INTERNATIONAL CONFERENCE ON SMART CITY AND SYSTEMS ENGINEERING (ICSCSE) | 2016年

关键词：

automobile fault diagnosis system; neural network; artificial fish swarm;

D O I：

10.1109/ICSCSE.2016.105

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

With the widespread use of automobiles, automobile engine fault diagnosis system is of great significance. According to the analysis of engine fault type and fault symptoms, 11 kinds of engine typical fault symptoms and fault reasons are extracted. Fault information is taken as training sample of neural network after the normalization. The optimized BP neural network based on artificial fish swarm algorithm is used in automobile engine fault diagnosis. The experiment results show that the improved BP neural network has good generalization ability and can effectively deal with different types of faults. The results are accurate and reliable, which can provide important reference for automobile fault diagnosis system design.

引用

页码：494 / 497

页数：4