TY - JOUR
T1 - Classification using radial basis function networks with uncertain weights
AU - Manson, G.
AU - Pierce, S.G.
AU - Worden, K.
PY - 2005
Y1 - 2005
N2 - This paper considers the performance of radial basis function neural networks for the purpose of data classification. The methods are illustrated using a simple two class problem. Two techniques for reducing the rate of misclassifications, via the introduction of an "unable to classify" label, are presented. The first of these considers the imposition of a threshold value on the classifier
outputs whilst the second considers the replacement of the crisp network weights with interval ranges. Two network training techniques are investigated and it is found that, although thresholding and uncertain weights give similar results, the level of variability of network performance is
dependent upon the training approach.
AB - This paper considers the performance of radial basis function neural networks for the purpose of data classification. The methods are illustrated using a simple two class problem. Two techniques for reducing the rate of misclassifications, via the introduction of an "unable to classify" label, are presented. The first of these considers the imposition of a threshold value on the classifier
outputs whilst the second considers the replacement of the crisp network weights with interval ranges. Two network training techniques are investigated and it is found that, although thresholding and uncertain weights give similar results, the level of variability of network performance is
dependent upon the training approach.
KW - damage detection
KW - interval arithmetic
KW - neural network
KW - radial basis function
UR - http://materials-science.ttp.net/0-87849-976-8/135/
UR - http://dx.doi.org/10.4028/0-87849-976-8.135
U2 - 10.4028/0-87849-976-8.135
DO - 10.4028/0-87849-976-8.135
M3 - Article
VL - 293-294
SP - 135
EP - 142
JO - Key Engineering Materials
JF - Key Engineering Materials
SN - 1013-9826
ER -