<?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%">Kovalnogov, Vladislav N.</style></author><author><style face="normal" font="default" size="100%">Fedorov, Ruslan V.</style></author><author><style face="normal" font="default" size="100%">Igor I. Shepelev</style></author><author><style face="normal" font="default" size="100%">Vyacheslav V. Sherkunov</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></authors></contributors><titles><title><style face="normal" font="default" size="100%">A novel quaternion linear matrix equation solver through zeroing neural networks with applications to acoustic source tracking</style></title><secondary-title><style face="normal" font="default" size="100%">AIMS Mathematics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">acoustic source tracking</style></keyword><keyword><style  face="normal" font="default" size="100%">dynamical 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%">quaternion</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%">2023</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.aimspress.com/article/doi/10.3934/math.20231323</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">11</style></number><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">25966-25989</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Due to its significance in science and engineering, time-varying linear matrix equation (LME) problems have received a lot of attention from scholars. It is for this reason that the issue of finding the minimum-norm least-squares solution of the time-varying quaternion LME (ML-TQ-LME) is addressed in this study. This is accomplished using the zeroing neural network (ZNN) technique, which has achieved considerable success in tackling time-varying issues. In light of that, two new ZNN models are introduced to solve the ML-TQ-LME problem for time-varying quaternion matrices of arbitrary dimension. Two simulation experiments and two practical acoustic source tracking applications show that the models function superbly.</style></abstract></record></records></xml>