Fuzzy Clustering Neural Network (FCNN): Competitive Learning and Parallel Architecture

Abstract

This article presents a fuzzy clustering neural network (FCNN) model that uses Gaussian nonlinearity. A learning algorithm, based on direct fuzzy competition between the nodes, is introduced. The connecting weights, which are adaptively updated in batch mode, converge towards values that are representative of the clustering structure of the input patterns. Mapping the proposed algorithm onto the corresponding architecture with three types of processing cells, it is feasible to implement the FCNN in parallel. The effectiveness of the FCNN is illustrated by applying the model to a number of test data sets, analyzing the hardware complexity of the architecture, and comparing the performance to that of fuzzy c-means (FCM) algorithm.