Fuzzy heterogeneous networks are recently introduced
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 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 paper compares the effectiveness of the fuzzy heterogeneous
model based on similarity with 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 performance, the effect of
working with crisp or fuzzy inputs, the use of traditional scalar
product 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.