A Generalized Regression Neural Network Based on Fuzzy Means Clustering and Its Application in System Identification

A method to simplify the generalized regression neural networks (GRNN) structure with a large numbers of training samples is proposed. The amount of pattern units is proportionate to the training samples. So in order to simplify the GRNN´s structure, some of the representative samples should be selected to build the network. This paper takes the fuzzy means clustering algorithm. It combines with a similarity measurement, which is calculated between input elements, to find the best clustering centers. According to the simulation results, this strategy can largely simplify the GRNN´s structure and significantly improve the network´s efficiency with just a tiny of loss in accuracy. The network structure built in this strategy can learn quickly, and is suitable to deal with the problems of nonlinear system identification.