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Proceedings Paper

Neural fault diagnosis techniques for nonlinear analog circuit
Author(s): Kurosh Madani; Amar Bengharbi; Veronique Amarger
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Paper 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.

Paper Details

Date Published: 4 April 1997
PDF: 12 pages
Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); doi: 10.1117/12.271511
Show Author Affiliations
Kurosh Madani, Univ. Paris XII (France)
Amar Bengharbi, Univ. Paris XII (France)
Veronique Amarger, Univ. Paris XII (France)

Published in SPIE Proceedings Vol. 3077:
Applications and Science of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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