Enhanced fuzzy-relational neural network with alternative relational products

Abstract

This paper describes an extension of fuzzy relational neural networks (FRNNs) that aims at improving their classification performance. We consider Pedrycz’s FRNN, which is one of the most effective and popular models. This model has traditionally used a single relational product (Circlet). The extension described in this paper consists in allowing applying other relational products in the training phase to the basic FRNN, looking to increase its predictive capabilities. The relational products considered for the extension are the so called BK-Products: SubTriangle, SupTriangle and Square; in addition, we propose the use of more general operators (t-norms and s-norms) in their definitions, which are also applied to the Circlet relational product. We explore the effectiveness of this extension in classification problems, through testing experiments on benchmark data sets with and without noise. Experimental results reveal that the proposed extension improves the classification performance of the basic FRNN, particularly in noisy data sets.