<?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%">SYED ABBAS</style></author><author><style face="normal" font="default" size="100%">ANIBAL CORONEL</style></author><author><style face="normal" font="default" size="100%">MANUEL PINTO</style></author><author><style face="normal" font="default" size="100%">SWATI TYAGI</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">EXPONENTIAL APPROXIMATION OF SOLUTIONS OF BIDIRECTIONAL NEURAL NETWORKS MODEL WITH POSITIVE DELAY</style></title><secondary-title><style face="normal" font="default" size="100%">Neural, Parallel, and Scientific Computations</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">approximate solution</style></keyword><keyword><style  face="normal" font="default" size="100%">delay differential equation</style></keyword><keyword><style  face="normal" font="default" size="100%">neural networks</style></keyword><keyword><style  face="normal" font="default" size="100%">piecewise constant argument of generalized type</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">01/2018</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://acadsol.eu/npsc/articles/26/1/1.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">26</style></volume><pages><style face="normal" font="default" size="100%">30</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;In this paper, a class of bidirectional neural network containing delay&amp;nbsp;has been studied. Based on the theory of differential calculus and the generalized&amp;nbsp;Gronwall inequality, the solution of the corresponding neural network model is approximated&amp;nbsp;using discretization method and a sufficient condition is established for&amp;nbsp;its stability. Some easily verifiable sufficient conditions are obtained ensuring that&amp;nbsp;every solution of the discrete-time analogue converges exponentially to the unique&amp;nbsp;solution. In the last section, two numerical example are also presented to validate the&amp;nbsp;feasibility of the proposed results and illustrate the advantages of the discrete-time&amp;nbsp;analogues in numerically simulating the continuous-time networks.&lt;/p&gt;
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