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The main findings of our work are given very briefly below. Detailed descriptions of our work and findings can be found in our publications.
- Since correlation is unable to deal with test spectra which
comprise combinations of template spectra, this method was not
considered suitable for this type of work.
- The numerical optimization examined here was also not found to be
satisfactory.
- SVD was superior to any method in dealing with test spectra that
were linearly-related to the template data. Unfortunately, it
performed worse than both the genetic algorithms and neural networks
for non-linearly related cases. While the iterative procedure devised
by us greatly improved matters, it was still not as good as the neural
networks. The SVD method has the added advantage of speed, test cases
can be run almost instantaneously, saving a considerable amount of
time.
- Neural networks were found to be good, especially for noisy test
data; no other method was superior in the presence of noise. However,
for larger datasets the training time became an issue.
- Genetic algorithms were found to be good for small datasets (less
than 20 spectra). However, analysis of the results needs more careful
consideration than any of the other methods. For example, normalized
test and template data had to be treated in a different way to the
equivalent non-normalized cases.
- We have shown that both SVD and neural networks can be used
effectively with large databases. Scaling up the number of
template spectra had some effects on the neural network, most
probably due to inability to train it to a small enough error.
However, SVD seemed unperturbed: provided the number of bins
describing a spectrum is greater than the number of template spectra,
the performance appears to be unaffected. The results from the neural
network and SVD were complementary: where the neural network gave poor
results, SVD gave good results and vice versa. Hence, these
results suggest that, an optimum spectral identification system would
be a neural network/SVD hybrid.