Information-gap robustness of a neural network regression model

S.G. Pierce, K. Worden, G. Manson

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


As a result of their black-box nature, neural networks resist traditional methods of certification and therefore cannot be used in safety critical applications. This situation is undesirable as neural networks can provide an effective solution to many engineering problems. The object of the current paper is to explore the possibility of quantifying and qualifying the reliability of neural networks by a means outside the traditional framework. The approach used here will follow Ben-Haim’s information-gap theory of uncertainty. This is a non-probabilistic approach which may lend itself well to certification of black-box systems. The approach is demonstrated here on a neural network regression model of the process of pre-sliding friction between solids.
Original languageEnglish
Title of host publication22nd IMAC Conference and Exposition 2004 (IMAC XXII): A Conference and Exposition on Structural Dynamics
Number of pages9
Publication statusPublished - 2004
Event22nd IMAC Conference and Exposition 2004 (IMAC XXII) - Dearborn, Michigan, United States
Duration: 26 Jan 200429 Jan 2004


Conference22nd IMAC Conference and Exposition 2004 (IMAC XXII)
Country/TerritoryUnited States
CityDearborn, Michigan


  • information-gap
  • robustness
  • neural network
  • regression model


Dive into the research topics of 'Information-gap robustness of a neural network regression model'. Together they form a unique fingerprint.

Cite this