Post-training on RBF neural networks

Radial basis function neural networks are the most widely used networks due to their rapid training, generality, and simplicity. The nature of these networks necessitates some types of errors which can never be removed by traditional training algorithms. This paper is an attempt to introduce the natural error sources of neural networks such as bias error, iteration-restricted error, and Gibbs error. Moreover, a new method is introduced, called post-training, to reduce these errors as far as desired.