<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Burkhard Lenze</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">HYPERBOLIC SIGMA-PI NEURAL NETWORK OPERATORS APPLIED TO IMAGE PROCESSING AND RECONSTRUCTION</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">26E40</style></keyword><keyword><style  face="normal" font="default" size="100%">41A05</style></keyword><keyword><style  face="normal" font="default" size="100%">41A63</style></keyword><keyword><style  face="normal" font="default" size="100%">92B20</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2002</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2002</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.acadsol.eu/en/articles/6/1/2.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">9</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In this paper, we recall how to design three-layer feedforward neural network operators based on hyperbolic sigma-pi units in order to act as approximation and interpolation devices for regular gridded data. The basic part of the paper consists of an application of this strategy in connection with image processing and reconstruction.&lt;/p&gt;
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