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

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.