Fuzzy Inputs and Missing Data in Similarity-Based Heterogeneous Neural Networks

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 eeectiveness of the fuzzy heterogeneous model based on similarity with that of the classical feed-forward one, in the context of an investigation in the eld of environmental sciences, namely, the geochemical study of natural waters in the Arctic (Spitzbergen). Classiication accuracy, the eeect 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.