Abstract:
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Fuzzy heterogeneous networks are recently introduced feed-forward
neural network models composed of neurons of a general class whose
inputs and weights are mixtures of continuous variables (crisp and/or
fuzzy) with discrete quantities, also admitting missing data. These
networks have net input functions based on similarity relations
between the inputs to and the weights of a neuron. They thus accept
heterogeneous --possibly missing-- inputs, and can be coupled with
classical neurons in hybrid network architectures, trained by means of
genetic algorithms or other evolutionary methods.
This report compares the effectiveness of the fuzzy heterogeneous
model based on similarity with that of the classical feed-forward one,
in the context of an investigation in the field of environmental
sciences, namely, the geochemical study of natural waters in the
Arctic (Spitzbergen). Classification accuracy, the effect of working
with crisp or fuzzy inputs, the use of traditional scalar product {em
vs.} similarity based functions, and the presence of missing data, are
studied.
The results obtained show that, from these standpoints, fuzzy
heterogeneous networks based on similarity perform better than classical
feed-forward models. This behaviour is consistent with previous
results in other application domains. |