Fault diagnosis of analog circuits with tolerances by using RBF and BP neural networks

Abstract

This paper presents a method for analog circuit fault diagnosis by using neural networks. This method exploits a DC approach for constructing a dictionary in fault diagnosis using the neural network's classification capability. Also, Radial Basis Function (RBF) and backward error propagation (BEP) networks are considered and compared for analog fault diagnosis. The primary focus of the paper is to provide robust diagnosis using a mechanism to deal with the problem of component tolerance and reduce testing time. Simulation results show that the radial basis function network with reasonable dimension has double precision in fault classification but its classification is local, while the backward error propagation network with reasonable dimension has single precision in fault classification but its classification is global.