Fuzzy Neural Network for Pattern Classification

Abstract

This research work proposes a fuzzy neural network (FNN) for pattern classification. The proposed network is the modified version of the Radial basis function neural network (RBFNN). FNN uses supervised fuzzy clustering and pruning algorithm to determine the precise number of clusters with proper centroid and width to form the processing nodes in the hidden layer. These clusters represent fuzzy set hyperspheres (FSHs), which are defined by the fuzzy membership function. The training between the hidden layer to output layer which is done by using the LMS algorithm in RBFNN is avoided, and the output is determined by using the fuzzy union operation. The fuzzy membership function shields the clustered patterns resulting in 100% accuracy for the data set used during training. Unlike other clustering algorithms used to construct the hidden layer of RBFNN, the proposed clustering algorithm is independent of tuning parameters and is fast in training and retrieval. Thus FNN reduces the computation time, guarantees 100% accuracy for any training set, and provides superior and comparable recognition accuracy for the datasets with the precise number of FSHs in the hidden layer. Hence the proposed FNN can be used for pattern classification.