<?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%">Vasilios N. Katsikis</style></author><author><style face="normal" font="default" size="100%">Jin, Long</style></author><author><style face="normal" font="default" size="100%">Mosić, Dijana</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Properties and computation of continuous-time solutions to linear systems</style></title><secondary-title><style face="normal" font="default" size="100%">Applied Mathematics and Computation</style></secondary-title><short-title><style face="normal" font="default" size="100%">Applied Mathematics and Computation</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">dynamical system</style></keyword><keyword><style  face="normal" font="default" size="100%">Generalized inverse</style></keyword><keyword><style  face="normal" font="default" size="100%">Gradient neural network</style></keyword><keyword><style  face="normal" font="default" size="100%">linear system</style></keyword><keyword><style  face="normal" font="default" size="100%">Zhang neural network</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.sciencedirect.com/science/article/pii/S0096300321003325</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">405</style></volume><isbn><style face="normal" font="default" size="100%">0096-3003</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We investigate solutions to the system of linear equations (SoLE) in both the time-varying and time-invariant cases, using both gradient neural network (GNN) and Zhang neural network (ZNN) designs. Two major limitations should be overcome. The first limitation is the inapplicability of GNN models in time-varying environment, while the second constraint is the possibility of using the ZNN design only under the presence of invertible coefficient matrix. In this paper, by overcoming the possible limitations, we suggest, in all possible cases, a suitable solution for a consistent or inconsistent linear system. Convergence properties are investigated as well as exact solutions.</style></abstract></record></records></xml>