Title :
Neural Network Integration Fusion Model and Application
Author :
Zhang, Xiaodan ; Tian, Feng ; Mu, Yuan ; Sun, Peigang ; Zhao, Hai
Author_Institution :
Shenyang Inst. of Aeronaut. Eng., Shenyang
Abstract :
A new fusion model is proposed, which is the combination of BP neural networks and D-S evidence reasoning, to solve the problems of low precision rate in automotive engine fault diagnosis by traditional expert system. The method realizes feature level fusion of all subjective data and expert experiments on different parts of engine, and the predominance compensation of different models. In simulation experiment, this method proposed in this paper can improve diagnosis precision 5.0% more than expert system.
Keywords :
backpropagation; case-based reasoning; engines; expert systems; fault diagnosis; mechanical engineering computing; BP neural networks; D-S evidence reasoning; automotive engine fault diagnosis; expert system; neural network integration fusion model; Aerospace engineering; Application software; Automotive engineering; Diagnostic expert systems; Engines; Fault diagnosis; Fuses; Neural networks; Reflection; Uncertainty;
Conference_Titel :
Natural Computation, 2007. ICNC 2007. Third International Conference on
Conference_Location :
Haikou
Print_ISBN :
978-0-7695-2875-5
DOI :
10.1109/ICNC.2007.496
