ON THE GREEDY RADIAL BASIS FUNCTION NEURAL NETWORKS FOR APPROXIMATION MULTIDIMENSIONAL FUNCTIONS

Authors

  • Reyadh S Naoum Department of Mathematics, College of Science, University of Baghdad.
  • Najla’a M Hussein Department of Computer Science, College of Science, University of Baghdad.

Keywords:

NON

Abstract

The aim of this paper is to approximate multidimensional functions by using the type of Feedforward neural networks (FFNNs) which is called Greedy radial basis function neural networks (GRBFNNs). Also, we introduce a modification to the greedy algorithm which is used to train the greedy radial basis function neural networks. An error bound are introduced in Sobolev space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result is published in [16]).

Published

2007-06-01

Issue

Section

Articles

How to Cite

(1)
ON THE GREEDY RADIAL BASIS FUNCTION NEURAL NETWORKS FOR APPROXIMATION MULTIDIMENSIONAL FUNCTIONS. ANJS 2007, 10 (1), 120-130.