A New Neural Network Classifier Based on Atanassov’s Intuitionistic Fuzzy Set Theory

Abstract

This paper proposes a new framework for training radial basis function neural networks (RBFNN). Determination of the centers of the Gaussian functions in the hidden layer of RBF neural network highly affects the performance of the network. This paper presents a novel radial basis function using fuzzy C-means clustering algorithm based on Atanassov’s intuitionistic fuzzy set (A-IFS) theory. The A-IFS theory takes into account another uncertainty parameter which is the hesitation degree that arises while defining the membership function and therefore, the cluster centers converge to more desirable locations than the cluster centers obtained using traditional fuzzy C-means algorithm. Furthermore, we make use of a new objective function obtained by Atanassov’s intuitionistic fuzzy entropy. This objective function is incorporated in the traditional fuzzy C-means clustering algorithm to maximize the good points in the class. The proposed method is used to improve the functionality of the Optimum Steepest Descent (OSD) learning algorithm. Adjusting RBF units in the network with great accuracy will result in better performance in fewer train iterations, which is essential when fast retraining of the network is needed, especially in the real-time systems. We compare the proposed Atanassov’s intuitionistic radial basis function neural network (A-IRBFNN) with fuzzy C-mean radial basis function neural network (FCMRBFNN) while both methods use OSD learning algorithm. Furthermore, the proposed A-IRBFNN is compared with other powerful fuzzy-based radial basis function neural network. Experimental results on Proben1 dataset and demonstrate the superiority of the proposed A-IRBFNN.