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Inversion of Uniform Field Eddy Current Data Using Neural Networks

  • J. M. Mann
  • L. W. Schmerr
  • J. C. Moulder
  • M. W. Kubovich
Chapter
Part of the Review of Progress in Quantitative Nondestructive Evaluation book series

Abstract

A resurgence of research in artificial neural networks has sparked interest in applying these networks to difficult computational tasks such as inversion. Artificial neural networks are composed of simple processing elements, richly interconnected. These networks can be trained to perform arbitrary mappings between sets of input-output pairs by adjusting the weights of interconnections. They require no a priori information or built-in rules; rather, they acquire knowledge through the presentation of examples. This characteristic allows neural networks to approximate mappings for functions which do not appear to have a clearly defined algorithm or theory. Neural network performance has proven robust when faced with incomplete, fuzzy, or novel data.

Keywords

Hide Layer Processing Element Automatic Gain Control Flaw Depth Flaw Dimension 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1990

Authors and Affiliations

  • J. M. Mann
    • 1
  • L. W. Schmerr
    • 1
  • J. C. Moulder
    • 1
  • M. W. Kubovich
    • 1
  1. 1.Center for NDEIowa State UniversityAmesUSA

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