<?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%">Jerbi, Houssem</style></author><author><style face="normal" font="default" size="100%">Aoun, Sondess Ben</style></author><author><style face="normal" font="default" size="100%">Abbassi, Rabeh</style></author><author><style face="normal" font="default" size="100%">Kchaou, Mourad</style></author><author><style face="normal" font="default" size="100%">Theodore E. Simos</style></author><author><style face="normal" font="default" size="100%">Spyridon D. Mourtas</style></author><author><style face="normal" font="default" size="100%">Vasilios N. Katsikis</style></author><author><style face="normal" font="default" size="100%">Shuai Li</style></author><author><style face="normal" font="default" size="100%">Cao, Xinwei</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An innovative neutrosophic logic adaptive high-order zeroing neural network for solving linear matrix equations: Applications to acoustic source tracking</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Computational and Applied Mathematics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Acoustic source localization</style></keyword><keyword><style  face="normal" font="default" size="100%">Fuzzy system</style></keyword><keyword><style  face="normal" font="default" size="100%">linear matrix equation</style></keyword><keyword><style  face="normal" font="default" size="100%">minimum-norm least-squares solution</style></keyword><keyword><style  face="normal" font="default" size="100%">Neutrosophic logic</style></keyword><keyword><style  face="normal" font="default" size="100%">Zeroing neural network</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2026</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.sciencedirect.com/science/article/pii/S0377042725005722</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">475</style></volume><pages><style face="normal" font="default" size="100%">117058</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Scholars have put a lot of emphasis on time-varying linear matrix equations (LMEs) problems because of its importance in science and engineering. The problem of determining the time-varying LME’s minimum-norm least-squares solution (MLLE) is therefore tackled in this work. This is achieved by the use of NHZNN, a recently developed neutrosophic logic/fuzzy adaptive high-order zeroing neural network technique. The NHZNN is an advancement on the conventional zeroing neural network (ZNN) technique, which has shown great promise in solving time-varying tasks. To address the MLLE task for arbitrary-dimensional time-varying matrices, three novel ZNN models are presented. The models perform exceptionally well, as demonstrated by two simulation studies and two real-world applications to acoustic source tracking.</style></abstract></record></records></xml>