Research on Neural Network Integration Fusion Method and Application on the Fault Diagnosis of Automotive Engine

摘要：

A new fusion model is proposed, which is the combination of integration BP neural networks models and D-S evidence reasoning model, to solve the problems of low precision rate in automotive engine fault diagnosis by traditional expert system. The method of this paper not only realizes feature level fusion of all subjective observation data and expert experiments on different parts of engineer, but also realizes the predominance compensation of different models. In simulation experiment, by comparison between the two methods, this method proposed in the paper can improve diagnosis precision 7.1 ％ more than expert system and reduce time complication degree.

作者: Xiaodan ZHANG Meng LU Peigang SUN Guixian XU Hai ZHAO

作者单位: Beijing Institute of Technology, China China Resources Land Limited Inc, China Shenyang Artillery Academy, China Northeastern University, China

会议类型: 国际会议

会议名称: 2nd IEEE Conference on Industrial Electronics and Applications(ICIEA 2007)(第二届IEEE工业电子与应用国际会议)

会议地点: 哈尔滨

会议语种:英文

在线出版日期: 2007-05-23（万方平台首次上网日期，不代表论文的发表时间）