Function approximation using a sinc neural network
Abstract
Neural networks for function approximation are the basis of many applications. Such networks often use a sigmoidal activation function (e.g. tanh) or a radial basis function (e.g. gaussian). Networks have also been developed using wavelets. In this paper, we present a neural network approximation of functions of a single variable, using sinc functions for the activation functions on the hidden units. Performance of the sinc network is compared with that of a tanh network with the same number of hidden units. The sinc network generally learns the desired inputoutput mapping in significantly fewer epochs, and achieves a much lower total error on the testing points. The original sinc network is based on theoretical results for function representation using the Whittaker cardinal function (an infinite series expansion in terms of sinc functions). Enhancements to the original network include improved transformation of the problem domain onto the network input domain. Further work is in progress to study the use of sinc networks for mappings in higher dimension.
 Publication:

Applications and Science of Artificial Neural Networks II
 Pub Date:
 March 1996
 DOI:
 10.1117/12.235959
 Bibcode:
 1996SPIE.2760..690E