Application of wavelet basis function neural networks for NDE

K. Hwang, Shreekanth Mandayam, S. S. Udpa, L. Udpa, W. Lord

Research output: Contribution to conferencePaper

9 Scopus citations

Abstract

This paper presents a novel approach for training a multiresolution, hierarchical wavelet basis function neural network. Such a network can be employed for characterizing defects in gas pipelines which are inspected using the magnetic flux leakage method of nondestructive testing. The results indicate that significant advantages over other neural network based defect characterization schemes could be obtained, in that the accuracy of the predicted defect profile can be controlled by the resolution of the network. The centers of the basis functions are calculated using a dyadic expansion scheme and a hybrid learning method. The performance of the network is demonstrated by predicting defect profiles from experimental magnetic flux leakage signals.

Original languageEnglish (US)
Pages1420-1423
Number of pages4
StatePublished - Dec 1 1996
EventProceedings of the 1996 IEEE 39th Midwest Symposium on Circuits & Systems. Part 3 (of 3) - Ames, IA, USA
Duration: Aug 18 1996Aug 21 1996

Other

OtherProceedings of the 1996 IEEE 39th Midwest Symposium on Circuits & Systems. Part 3 (of 3)
CityAmes, IA, USA
Period8/18/968/21/96

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

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