Neural fault diagnosis techniques for non linear analogue circuits

Comparing to the progress accomplished in the area of digital circuits and systems, the analogue circuits fault diagnosis field is still its infancy. Recently, some approaches to analog circuit's fault diagnosis have been proposed using pattern recognition capability of artificial neural networks. However, the major of these papers have analysed linear analog circuits including resistors exclusively. In this paper, we present several neural network based approaches to analog circuits fault diagnosis using Back-Propagation (BP), Learning Vector Quantization (LVQ) and Radial Basis Function (RBF) neural models. The interest of our approaches is related to fact that we use competitive multi-neural network architecture. Case study, simulations results and experimental validation of presented techniques have been reported.