AdaBoost based ensemble of neural networks in analog circuit fault diagnosis

In neural network based analog circuit fault diagnosis methods, there exists big test error even when the training error reaches zero. It is owing to the bad generalization performance of neural network classifier. In order to improve the diagnosis system based on generalization performance of neural network in analog circuit fault isolation, an AdaBoost based ensemble of neural network (ENN) system is proposed. The bias-variance decomposition shows that AdaBoost resample technique can reduce the correlation of the component networks, cut down the variance and generalization error of the ensemble networks. Component neural networks in this ensemble are trained by their respective training sets created by AdaBoost resample technique. Data sets obtained from simulation and actual circuit were used to evaluate this ensemble system, and experimental results reveal the validity of this method and the improvement of fault diagnosis accuracy.