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Neural Computation, Vol 9, 1109-1126, Copyright © 1997 by The MIT Press

LETTERS

The Faulty Behavior of Feedforward Neural Networks with Hard-limiting Activation Fuction

Zhiyu Tian, Ting -Ting Y. Lin, Shiyuan Yang and Shibai Tong

With the progress in hardware implementation of artificial neural net-works, the ability to analyze their faulty behavior has become increasingly important to their diagnosis, repair, reconfiguration, and reliable application. The behavior of feedforward neural networks with hard-limiting activation function under stuck-at faults is studied in this article. It is shown that the stuck-at-M faults have a larger effect on the network's performance than the mixed stuck-at faults, which in turn have a larger effect than that of stuck-at-0 faults. Furthermore, the fault-tolerant ability of the network decreases with the increase of its size for the same percentage of faulty interconnections. The results of our analysis are validated by Monte-Carlo simulations.




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J COGNITIVE NEUROSCIENCE NEURAL COMPUTATION MIT PRESS JOURNALS
Copyright © 1997 by The MIT Press.