Comparing different clustering techniques-RBF networks training

Abstract

Clustering techniques have a strong influence on the performance achieved by RBF neural networks. The article compares the performance achieved by RBF networks using seven different clustering techniques. For such, different sizes of RBF networks are trained and tested using an automatic target recognition data set. The performances of these RBF networks using each clustering technique are compared and analyzed.