Fault characterization of a multilayered perceptron network

The results of a set of simulation experiments conducted to quantify the effects of faults in a classification network implemented as a three-layered perceptron model are reported. The percentage of vectors misclassified by the classification network, the time taken for the network to stabilize, and the output values are measured. The results show that both transient and permanent faults have a significant impact on the performance of the network. Transient faults are also found to cause the network to be increasingly unstable as the duration of a transient is increased. The average percentage of the vectors misclassified is about 25%; after relearning, this is reduced to 10%. The impact of link faults is relatively insignificant in comparison with node faults (1% versus 19% misclassified after relearning). A study of the impact of hardware redundancy shows a linear increase in misclassifications with increasing hardware size