Fault diagnosis in analog circuits based on PCA、rough set and neural networks
Zhang Bia · Automation and Instrumentation · 2009
Directed toward fault diagnosis in analog circuits based on neural networks,feature parameters make its fault diagnostic difficult.In order to solve this problem,we present a method of neural network fault diagnosis is based on PCA、Rough Sets and Neural Networks.And Rough Sets theory is used to eliminate unnessary attributes from the decision table,Feature parameters of the step response are compressed using principal component analysis(PCA).The simulation experiments shows that it has many good properties,such as simplifying the structure of NN,improving the training speed and fault coverage,which obviously quickens training speed and decreases training time,and the application effect is notable.