Research on Airborne Equipment Fault Diagnosis Method Based on Rough Set-Neural Network
Yao Jia-jia · Avionics Technology · 2008
In this paper we present a new fault diagnosis method based on rough set-neural network.Rough set theory combined with neural networks can be applied to fault diagnosing of airborne equipment.The original fault diagnosis samples are processed by using rough set theory.According to the decision attribute positive region size of condition attribute(s),the minimum fault feature subset is selected,and thus the neural network topology structure is determined.The networks well trained can establish the mapping relationship between inputs and outputs,which is used to realize the fault diagnosis.The effectiveness of the method is proved by a simulation example.