Fuzzy heterogeneous networks based on similarity are recently introduced
feed-forward neural network models composed by neurons of a general class
whose inputs are mixtures of continuous (crisp and/or fuzzy) with discrete
quantities, admitting also missing data. These networks have
activation functions based on similarity relations between inputs and neuron weights. They can be coupled with classical neurons in
hybrid network architectures, trained with genetic algorithms.
This paper compares the effectivity of this fuzzy heterogeneous
model based on similarity with the classical feed-forward one (scalar-product
driven and using crisp quantities) in a time-series prediction setting.
The results obtained show a remarkable increasing performance when
departing from the classical neuron and a comparable one when
confronted with other current powerful techniques, such as the FIR
methodology.