Paper Title:
Considerations for Practical Neural Network Application to a Damage Detection Problem
  Abstract

The application of a multilayer perceptron (MLP) neural network to a damage location problem on a GNAT aircraft wing is considered. The problems associated with effective network training and evaluation are discussed, focussing on ensuring good generalisation performance of the network to the classification of new data. Both conventional Maximum Likelihood and Bayesian Evidence based training techniques are considered and a simple thresholding technique is presented to aid in the rejection of poorly regularised network structures. Examples are presented for an artificial simple 2 class problem (drawn from a Gaussian distribution) and a real 9 class problem on the GNAT aircraft wing.

  Info
Periodical
Key Engineering Materials (Volumes 293-294)
Edited by
W.M. Ostachowicz, J.M. Dulieu-Barton, K.M. Holford, M. Krawczuk and A. Zak
Pages
151-158
DOI
10.4028/www.scientific.net/KEM.293-294.151
Citation
G. Pierce, K. Worden, G. Manson, "Considerations for Practical Neural Network Application to a Damage Detection Problem ", Key Engineering Materials, Vols. 293-294, pp. 151-158, 2005
Online since
September 2005
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Price
$32.00
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