Reinders A ,
Visser T ,
Roos D ,
Vink PJF de ,
Luinge HJ
27 p
in Dutch
1991
Toon Nederlands
English Abstract This document describes how neural networks can be
trained to classify and recognize infrared spectra. Backpropagation was
used as the neural network type. The effect of noise on the recognition
capabilities of a network has been investigated by generating 150 spectra
with various noise levels out of 3 standard spectra. The trained network
appeared to be capable of recognizing spectra correctly up to a noise level
of 70%. Recognition appears to be correct up to a noise lebel of 70%. The
classifying capabilities of backpropagation of spectra have been studied by
training a network with 30 spectra, equally divided over three classes.
Fourteen other spectra were used as a control set. Only one spectrum was
found to be incorrectly classified. The preliminary conclusion is that
neural networks are a useful addition to standard pattern matching
techniques, especially for recognizing visual aspects.
