Robustness of feedforward neural networks

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages783-788
Number of pages6
Volume1993-January
ISBN (Print)0780309995
DOIs
StatePublished - 1993
EventIEEE International Conference on Neural Networks, ICNN 1993 - San Francisco, United States
Duration: Mar 28 1993Apr 1 1993

Other

OtherIEEE International Conference on Neural Networks, ICNN 1993
CountryUnited States
CitySan Francisco
Period3/28/934/1/93

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Artificial Intelligence

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    Chiu, C. T., Mehrotra, K., Mohan, C. K., & Ranka, S. (1993). Robustness of feedforward neural networks. In IEEE International Conference on Neural Networks - Conference Proceedings (Vol. 1993-January, pp. 783-788). [298655] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICNN.1993.298655