<?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%">CHENGJUN GUO</style></author><author><style face="normal" font="default" size="100%">DONAL O’REGAN</style></author><author><style face="normal" font="default" size="100%">FEIQI DENG</style></author><author><style face="normal" font="default" size="100%">RAVI P.AGARWAL</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">FIXED POINTS AND EXPONENTIAL STABILITY FOR UNCERTAIN STOCHASTIC NEURAL NETWORKS WITH MULTIPLE MIXED TIME-DELAYS</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">34K50</style></keyword><keyword><style  face="normal" font="default" size="100%">60H10</style></keyword><keyword><style  face="normal" font="default" size="100%">93Dxx</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.acadsol.eu/en/articles/19/2/3.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">19</style></volume><pages><style face="normal" font="default" size="100%">17</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 we study the stability of a stochastic neural networks with parameter uncertainties and multiple time delays $${ dx \ \ = [−(A + △A(t))x(t) + (B + △B(t))f(t, x(t), x(t − τ_1(t)), . . . , x(t − τ_m(t)))}$$ $${ + \sum^{k}_{p=1} (W_p + △W_p(t)) \int^t_{t−r_p(t)} g_p(x(s))ds]dt + \sum^l_{j=1} h_j (t, x(t), x(t − σ_j (t)))dw(t).}$$ Using fixed point theory and a linear matrix inequality(LMI), we obtain new criteria for exponential stability in mean square of the considered uncertain stochastic neural networks with multiple mixed time-delays.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><section><style face="normal" font="default" size="100%">191</style></section></record></records></xml>