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 e ectivity 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.Peer ReviewedPostprint (author's final draft
