Robustness of feedforward neural networks

Ching Tai Chiu, Kishan Mehrotra, Chilukuri K. Mohan, Sanjay Ranka

Research output: Chapter in Book/Entry/PoemConference contribution

16 Scopus citations


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.

Original languageEnglish (US)
Title of host publication1993 IEEE International Conference on Neural Networks, ICNN 1993
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)0780309995
StatePublished - 1993
EventIEEE International Conference on Neural Networks, ICNN 1993 - San Francisco, United States
Duration: Mar 28 1993Apr 1 1993

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576


OtherIEEE International Conference on Neural Networks, ICNN 1993
Country/TerritoryUnited States
CitySan Francisco

ASJC Scopus subject areas

  • Software


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