On the generalization of incremental learning RBF neural networks trained with significant patterns

This paper presents some new results on the generalization of incremental learning radial basis function neural networks which are trained with selected significant samples from the input space. Our main focus is to show that we need to pick the right proportion of significant samples from the input space which not only generate an optimal size network but also ensure an acceptable generalization accuracy for an application. Experimental results on these data sets reveal that training with significant patterns of various proportions has greater influence on the generalization ability of the RBF networks.