An adaptive deep belief network for fault diagnosis of complex analog circuits

Aiming at the problems of long pre-training time-consuming and poor diagnostic accuracy during unsupervised training of traditional DBN, an adaptive deep belief network (ADBN ) is proposed for analog circuit fault diagnosis. The adaptive learning rate is proposed according to the similarity and difference of the parameter updating direction to improve the convergence speed of the network. The ADBN is applied to the diagnosis experiments of a two-stage four-op-amp dual second-order low-pass filter, and the experimental results show that the proposed ADBN can realize the classification and localization of difficult faults by guaranteeing the classification accuracy and higher diagnosis rate based on the fast convergence speed.