Several methods have been applied to the prediction of ozone
concentration. In this work, an Heterogeneous Neural Network (HNN)
is used to perform the same task. Different capabilities of HNN
are exploited like imprecision in data or flexibility in the
function computed by the neurons. The results obtained are
compared with previous methodologies like Multi-layer Perceptron
(MLP), Elman Network (EN), Modified Elman Network (MEN), Fuzzy
Inductive Reasoning (FIR) and Long Short Term Memory Recurrent
Neural Network (LSTM). Without being a Recurrent Network, HNN is
able to get similar results than other methodologies, not far from
them. More complex and specialized similarity functions can be
developed in order to reach higher performances.Postprint (published version
