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dc.contributor.authorElwasif, Wael R.
dc.contributor.authorFausett, Laurene V
dc.date.accessioned2017-10-05T20:02:15Z
dc.date.available2017-10-05T20:02:15Z
dc.date.issued1996-03-22
dc.identifier.citationElwasif, W. R., & Fausett, L. V. (1996). Function approximation using a sinc neural network. Paper presented at the Proceedings of SPIE - the International Society for Optical Engineering, , 2760 690-701.en_US
dc.identifier.urihttp://hdl.handle.net/11141/1777
dc.descriptionSigmoid activation function, Sinc networksen_US
dc.description.abstractNeural 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.en_US
dc.language.isoen_USen_US
dc.rightsThis published article is made available in accordance with publishers policy. It may be subject to U.S. copyright law.en_US
dc.rights.urihttp://spie.org/publications/journals/guidelines-for-authors#Terms_of_Useen_US
dc.titleFunction approximation using a sinc neural networken_US
dc.typeConference Proceedingen_US
dc.identifier.doi10.1117/12.235959


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