A method of cloud-sample control and generation with application to circuit fault diagnosis
一种云样本的控制产生及在电路故障诊断中的应用

To select feature samples in circuit fault diagnosis,we propose a method of cloud-sample generation,and apply it to artificial-neural-network training and recognition.First,the inverse cloud model theory is employed to obtain the statistical digital feature of the samples,and then the extended training data set is produced by positive cloud theory. Second,two kinds of networks are trained with the newly produced data set.Simulation results reveal that the performance of the neural network trained by the cloud samples is better than that trained by the conventional methods.The results also proved that the network is robust to random noise,and the proposed method is valid in the faults diagnosis of analog circuit.