Najla, , M. Hussein, A., S. Naoum, R. (2007). ON THE GREEDY RADIAL BASIS FUNCTION NEURAL NETWORKS FOR APPROXIMATION MULTIDIMENSIONAL FUNCTIONS. , 10(1), 120-130.
Najla; a M. Hussein; Reyadh S. Naoum. "ON THE GREEDY RADIAL BASIS FUNCTION NEURAL NETWORKS FOR APPROXIMATION MULTIDIMENSIONAL FUNCTIONS". , 10, 1, 2007, 120-130.
Najla, , M. Hussein, A., S. Naoum, R. (2007). 'ON THE GREEDY RADIAL BASIS FUNCTION NEURAL NETWORKS FOR APPROXIMATION MULTIDIMENSIONAL FUNCTIONS', , 10(1), pp. 120-130.
Najla, , M. Hussein, A., S. Naoum, R. ON THE GREEDY RADIAL BASIS FUNCTION NEURAL NETWORKS FOR APPROXIMATION MULTIDIMENSIONAL FUNCTIONS. , 2007; 10(1): 120-130.

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

Journal of Al-Nahrain University - Science
Article 1, Volume 10, Issue 1, 2007, Pages 120-130
Authors
; a M. Hussein; Reyadh S. Naoum
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]).
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