@article {IOPORT.01993932,
    author       = {El-Gamal, M. A.},
    title        = {Genetically evolved neural networks for fault classification in analog circuits.},
    year         = {2002},
    journal      = {Neural Computing and Applications},
    volume       = {11},
    number       = {2},
    issn         = {0941-0643},
    pages        = {112-121},
    publisher    = {Springer-Verlag, London},
    doi          = {10.1007/s005210200023},
    abstract     = {Summary: 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.},
    msc2010      = {I.2.6},
    identifier   = {01993932},
}