Merging and Constrained Learning for Interpretability in Neuro-Fuzzy Systems

A methodology for development of linguistically interpretable fuzzy models from data is developed. The implementation of the model is conducted through the training of a neuro-fuzzy network. Structure of the model is firstly obtained by subtractive clustering, allowing the extraction of a set of relevant rules from input-output data. The model parameters are then tuned via the training of a neural network through backpropagation. Interpretability goals are pursued through membership function merging and some constrains on the tuning of parameters. The assignment of linguistic labels to each of the membership functions is then possible. The model obtained for the system under analysis can be described, in this way, by a set of linguistic rules, easily interpretable.