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Proceedings Paper

Function approximation using a sinc neural network
Author(s): Wael R. Elwasif; Laurene V. Fausett
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Paper 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 input-output 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.

Paper Details

Date Published: 22 March 1996
PDF: 12 pages
Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); doi: 10.1117/12.235959
Show Author Affiliations
Wael R. Elwasif, Florida Institute of Technology (United States)
Laurene V. Fausett, Florida Institute of Technology (United States)

Published in SPIE Proceedings Vol. 2760:
Applications and Science of Artificial Neural Networks II
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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