Blending application of rough sets and neural network in EFI engine fault diagnosis
Chenguang Cai · Journal of Chongqing Jiaotong University · 2006
The information of reflecting the EFI engine running state is complicated,the relationship between fault and numerous stating information is uncertain and fuzzy.The key of EFI engine fault diagnosis process is how to acquiring and using valuable information from those complicated multi-source information.In this paper,rough sets theory was applied to reduction of incomplete information to find necessary conditions for diagnosis,and according to the results of reduction the fault diagnosis system of based on neural networks was founded.The comparison results of network training indicated that the structure of neural network was simplified by reduction process of based on rough sets theory and efficiency of network training was enhanced;the feasibility of application of rough sets integrated with neural networks to fault diagnosis of EFI engine was verified by the practical example.