On the greedy ridge function neural networks for approximation multidimensional functions

Abstract EN

The aim of this paper is to approximate multidimensional functions f ∈ C (Rs) by developing a new type of Feedforward neural networks (FFNNs) which we called it Greedy ridge function neural networks (GRGFNNs).

Also, we introduce a modification to the greedy algorithm which is used to train the greedy ridge 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 in [1]).