Genetically Evolved Neural Networks for Fault Classification in Analog Circuits |
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Authors: | M A El-Gamal |
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Affiliation: | (1) Department of Engineering Physics & Mathematics, Faculty of Engineering Cairo University, Giza, Egypt, EG |
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Abstract: | A new fault classification system for analog circuits is presented. The proposed system utilises the pattern recognition potential
of neural networks and the population-based search strategy of genetic algorithms in detecting and isolating faults in analog
circuits. Features that characterise the circuit behaviour under fault-free and fault situations are first simulated or measured.
An unsupervised fault-grouping algorithm that estimates the overlaps between different faults in the features space is then
introduced. Accordingly, a suitable training set is constructed and employed to train a population of genetically evolved
neural networks to recognise circuit faults. A two-phase analog fault classification strategy is also developed. Experimental
results demonstrate the high classification accuracy of the proposed system.
ID="A1" Correspondence and offprint requests to: M.A. El-Gamal, Department of Engineering Physics Mathematics, Cairo University, Giza, Egypt Email: mhgamal@alpha1-eng.cairo.eun.eg |
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Keywords: | : Analog circuits Fault classification Fault grouping Fault simulation Genetic algorithms Genetically evolved neural networks |
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