Comparison between traditional neural networks and radial basis function networks

Abstract

The paper presents the properties of two types of neural networks: traditional neural networks and radial basis function (RBF) networks, both of which are considered as universal approximators. In this paper, the advantages and disadvantages of the two types of neural network architectures are analyzed and compared based on four different examples. The comparison results indicate approaches to be taken relative to the network model selection for practical applications.