<?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%">Predrag S. Stanimirović</style></author><author><style face="normal" font="default" size="100%">Spyridon D. Mourtas</style></author><author><style face="normal" font="default" size="100%">Mosić, Dijana</style></author><author><style face="normal" font="default" size="100%">Vasilios N. Katsikis</style></author><author><style face="normal" font="default" size="100%">Cao, Xinwei</style></author><author><style face="normal" font="default" size="100%">Shuai Li</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Zeroing neural network approaches for computing time-varying minimal rank outer inverse</style></title><secondary-title><style face="normal" font="default" size="100%">Applied Mathematics and Computation</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Dynamic system</style></keyword><keyword><style  face="normal" font="default" size="100%">Generalized inverse</style></keyword><keyword><style  face="normal" font="default" size="100%">Matrix equation</style></keyword><keyword><style  face="normal" font="default" size="100%">Minimal rank outer inverse</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%">2024</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.sciencedirect.com/science/article/pii/S0096300323005817</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">465</style></volume><pages><style face="normal" font="default" size="100%">128412</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Generalized inverses are extremely effective in many areas of mathematics and engineering. The zeroing neural network (ZNN) technique, which is currently recognized as the state-of-the-art approach for calculating the time-varying Moore-Penrose matrix inverse, is investigated in this study as a solution to the problem of calculating the time-varying minimum rank outer inverse (TV-MROI) with prescribed range and/or TV-MROI with prescribed kernel. As a result, four novel ZNN models are introduced for computing the TV-MROI, and their efficiency is examined. Numerical tests examine and validate the effectiveness of the introduced ZNN models for calculating TV-MROI with prescribed range and/or prescribed kernel.</style></abstract></record></records></xml>