A Novel Fuzzy Rough Granular Neural Network for Classification

Abstract

A novel fuzzy rough granular neural network (NFRGNN) based on the multilayer perceptron using back-propagation algorithm is described for fuzzy classification of patterns. We provide a development strategy of knowledge extraction from data using fuzzy rough set theoretic techniques. Extracted knowledge is then encoded into the network in the form of initial weights. The granular input vector is presented to the network while the target vector is provided in terms of membership values and zeros. The superiority of NFRGNN is demonstrated on several real life data sets.