Fault diagnosis based on heterogeneous rough neural network ensemble
Xianghua Fu · Machinery Design and Manufacture · 2006
HRNNE can improve the generation capacity of the neural networks evidently,but also need not to determine the structures of the neural networks beforehand.It is very easy to use in practice.We design four different diagnosis classifiers to compare the diagnosis performance in the standard sample set of the fuel injection system of diesel engine.The experiment results show that HRNNE is the best one,which get high diagnosis accuracy.