Intrinsic Error Sources of Neural Networks

The nature of radial basis function (RBF) 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