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Experimental fault characterization of a neural networkThe effects of a variety of faults on a neural network is quantified via simulation. The neural network consists of a single-layered clustering network and a three-layered classification network. The percentage of vectors mistagged by the clustering network, the percentage of vectors misclassified by the classification network, the time taken for the network to stabilize, and the output values are all measured. The results show that both transient and permanent faults have a significant impact on the performance of the measured network. The corresponding mistag and misclassification percentages are typically within 5 to 10 percent of each other. The average mistag percentage and the average misclassification percentage are both about 25 percent. After relearning, the percentage of misclassifications is reduced to 9 percent. In addition, transient faults are found to cause the network to be increasingly unstable as the duration of a transient is increased. The impact of link faults is relatively insignificant in comparison with node faults (1 versus 19 percent misclassified after relearning). There is a linear increase in the mistag and misclassification percentages with decreasing hardware redundancy. In addition, the mistag and misclassification percentages linearly decrease with increasing network size.
Document ID
Document Type
Contractor Report (CR)
Tan, Chang-Huong
(Illinois Univ. Urbana-Champaign, IL, United States)
Date Acquired
September 6, 2013
Publication Date
August 1, 1990
Subject Category
Computer Systems
Report/Patent Number
NAS 1.26:186885
Accession Number
Funding Number(s)
Distribution Limits
Work of the US Gov. Public Use Permitted.
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