4 April 1997 Neural fault diagnosis techniques for nonlinear analog circuit
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Abstract
Comparing to the progress accomplished in the area of digital circuits and systems, the analogue circuits fault diagnosis field is still in 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 analyzed linear analog circuits including resistors exclusively. In this paper, we present several neural network based approaches to analog circuits fault diagnosis using Back-Propagation, Learning Vector Quantization and Radial Basis Function neural models. The interest of our approaches is related to the fact that we use competitive multi-neural network architecture. Case study, simulation results and experimental validation of presented techniques have been reported.
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Kurosh Madani, Amar Bengharbi, Veronique Amarger, "Neural fault diagnosis techniques for nonlinear analog circuit", Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); doi: 10.1117/12.271511; https://doi.org/10.1117/12.271511
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