Abstract
This paper presents a comparative analysis of neural networks, simulated annealing, and genetic algorithms in the determination of input patterns for testing analog circuits. The problem has been modelled as an optimization problem in which the objective is to determine a test signal that maximizes the quadratic difference between the nominal response and the faulty one due to a defect in the circuit. This approach makes possible the search of the test pattern space by using techniques based on neural and evolutive algorithms.
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© 1995 Springer-Verlag Berlin Heidelberg
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Bernier, J.L., Merelo, J.J., Ortega, J., Prieto, A. (1995). Test pattern generation for analog circuits using neural networks and evolutive algorithms. In: Mira, J., Sandoval, F. (eds) From Natural to Artificial Neural Computation. IWANN 1995. Lecture Notes in Computer Science, vol 930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-59497-3_258
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DOI: https://doi.org/10.1007/3-540-59497-3_258
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