@inproceedings{b5534e8cff13481a9ca1aa7d8e7bc3f8,
title = "Robustness of feedforward neural networks",
abstract = "Many artificial neural networks in practical use can be demonstrated not to be fault tolerant; this can result in disasters when localized errors occur in critical parts of these networks. In this paper, we develop methods for measuring the sensitivity of links and nodes of a feedforward neural network and implement a technique to ensure the development of neural networks that satisfy well-defined robustness criteria. Experimental observations indicate that performance degradation in our robust feedforward network is significantly less than a randomly trained feedforward network of the same size by an order of magnitude.",
author = "Chiu, {Ching Tai} and Kishan Mehrotra and Mohan, {Chilukuri K.} and Sanjay Ranka",
note = "Publisher Copyright: {\textcopyright} 1993 IEEE.; IEEE International Conference on Neural Networks, ICNN 1993 ; Conference date: 28-03-1993 Through 01-04-1993",
year = "1993",
doi = "10.1109/ICNN.1993.298655",
language = "English (US)",
series = "IEEE International Conference on Neural Networks - Conference Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "783--788",
booktitle = "1993 IEEE International Conference on Neural Networks, ICNN 1993",
}