Generating Singleton Fuzzy Models from Data with Interpretable Partition

Abstract

This paper presents a new methodology for obtaining singleton fuzzy model from experimental data. Each input variable is partitioned into triangular membership functions so that consecutive fuzzy sets exhibit and specific overlapping of 0.5. The recursive least squares method is employed to adjust the singleton consequences and the gradient descent method is employed to update only the modal value of each triangular membership function to preserve the overlap and reducing the number of parameters to be estimated. Applications to a function approximation problem and to a pattern classification problem are illustrated.