<?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%">Vasilios N. Katsikis</style></author><author><style face="normal" font="default" size="100%">Spyridon D. Mourtas</style></author><author><style face="normal" font="default" size="100%">Predrag S. Stanimirović</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%">Time-varying minimum-cost portfolio insurance problem via an adaptive fuzzy-power LVI-PDNN</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%">Fuzzy logic system</style></keyword><keyword><style  face="normal" font="default" size="100%">Neural networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Portfolio insurance</style></keyword><keyword><style  face="normal" font="default" size="100%">portfolio optimization</style></keyword><keyword><style  face="normal" font="default" size="100%">Time-varying linear programming</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.sciencedirect.com/science/article/pii/S0096300322007688</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">441</style></volume><pages><style face="normal" font="default" size="100%">127700</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">It is well known that minimum-cost portfolio insurance (MPI) is an essential investment strategy. This article presents a time-varying version of the original static MPI problem, which is thus more realistic. Then, to solve it efficiently, we propose a powerful recurrent neural network called the linear-variational-inequality primal-dual neural network (LVI-PDNN). By doing so, we overcome the drawbacks of the static approach and propose an online solution. In order to improve the performance of the standard LVI-PDNN model, an adaptive fuzzy-power LVI-PDNN (F-LVI-PDNN) model is also introduced and studied. This model combines the fuzzy control technique with LVI-PDNN. Numerical experiments and computer simulations confirm the F-LVI-PDNN model’s superiority over the LVI-PDNN model and show that our approach is a splendid option to accustomed MATLAB procedures.</style></abstract></record></records></xml>