<?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%">SNEZHANA HRISTOVA</style></author><author><style face="normal" font="default" size="100%">PETER KOPANOV</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">STABILITY OF NEURAL NETWORKS WITH RANDOM IMPULSES</style></title><secondary-title><style face="normal" font="default" size="100%">Dynamic Systems and Applications</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">34A37</style></keyword><keyword><style  face="normal" font="default" size="100%">34D20</style></keyword><keyword><style  face="normal" font="default" size="100%">34F99</style></keyword><keyword><style  face="normal" font="default" size="100%">92B20</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%">10/2018</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://acadsol.eu/dsa/articles/27/4/7.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">12</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;One of the main properties of solutions of neural networks is stability&amp;nbsp;and often the direct Lyapunov method is used to study stability properties. We consider the Hopfield’s graded response neural network in the case when the neurons are&amp;nbsp;subject to a certain impulsive state displacement at random exponentially distributed&amp;nbsp;moments. It changes significantly the behavior of the solutions because they are not&amp;nbsp;deterministic ones but they are stochastic processes. We examine the stability of the&amp;nbsp;equilibrium of the model. Some sufficient conditions for p-moment stability of equilibrium of neural networks with time varying self-regulating parameters of all units&amp;nbsp;and time varying functions of the connection between two neurons in the network are&amp;nbsp;obtained. These sufficient conditions are explicitly expressed in terms of the parameters of the system and hence they are easily verifiable. We illustrate our theory on&amp;nbsp;a particular nonlinear neural network.&lt;/p&gt;
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