ML Anthology

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

Zhiyu Tian, Ting-Ting Y. Lin, Shiyuan Yang, Shibai Tong
NeCo 1997 pp. 1109-1126
doi:10.1162/NECO.1997.9.5.1109 /neco/1997/tian1997neco-faulty/

Abstract

With the progress in hardware implementation of artificial neural networks, 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.

NeCo Semantic Scholar

Cite

Text

Tian et al. "The Faulty Behavior of Feedforward Neural Networks with Hard-Limiting Activation Fuction." Neural Computation, 1997. doi:10.1162/NECO.1997.9.5.1109

Markdown

[Tian et al. "The Faulty Behavior of Feedforward Neural Networks with Hard-Limiting Activation Fuction." Neural Computation, 1997.](https://mlanthology.org/neco/1997/tian1997neco-faulty/) doi:10.1162/NECO.1997.9.5.1109

BibTeX

@article{tian1997neco-faulty,
  title     = {{The Faulty Behavior of Feedforward Neural Networks with Hard-Limiting Activation Fuction}},
  author    = {Tian, Zhiyu and Lin, Ting-Ting Y. and Yang, Shiyuan and Tong, Shibai},
  journal   = {Neural Computation},
  year      = {1997},
  pages     = {1109-1126},
  doi       = {10.1162/NECO.1997.9.5.1109},
  volume    = {9},
  url       = {https://mlanthology.org/neco/1997/tian1997neco-faulty/}
}