Robustness of feedforward neural networks

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

Research output: Chapter in Book/Entry/PoemConference contribution

17 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 publicationIEEE International Conference on Neural Networks - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)0780309995
StatePublished - 1993
EventIEEE International Conference on Neural Networks, ICNN 1993 - San Francisco, United States
Duration: Mar 28 1993Apr 1 1993


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

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Artificial Intelligence


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