Evaluation of neural network robust reliability using information-gap theory

S.G. Pierce, Y. Ben-Haim, K. Worden, G. Manson

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28 Citations (Scopus)


A novel technique for the evaluation of neural network robustness against uncertainty using a nonprobabilistic approach is presented. Conventional optimization techniques were employed to train multilayer perceptron (MLP) networks, which were then probed with an uncertainty analysis using an information-gap model to quantify the network response to uncertainty in
the input data. It is demonstrated that the best performing network on data with low uncertainty is not in general the optimal network on data with a higher degree of input uncertainty. Using the concepts of information-gap theory, this paper develops a theoretical framework for information-gap uncertainty applied to neural networks, and explores the practical application of the procedure to
three sample cases. The first consists of a simple two-dimensional (2-D) classification network operating on a known Gaussian distribution, the second a nine-lass vibration classification problem from an aircraft wing, and the third a two-class example from a database of breast cancer incidence.
Original languageEnglish
Pages (from-to)1349-1361
Number of pages12
JournalIEEE Transactions on Neural Networks
Issue number6
Publication statusPublished - 2006


  • Information-gap
  • robustness
  • neural networks
  • uncertainty
  • gaussian distribution
  • vibration classification
  • uncertainty analysis
  • nonprobabilistic method
  • neural network robust reliability
  • multilayer perceptron network
  • information-gap theory
  • breast cancer incidence
  • aircraft wing

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