<?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%">Theodore E. Simos</style></author><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></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multi-input bio-inspired weights and structure determination neuronet with applications in European Central Bank publications</style></title><secondary-title><style face="normal" font="default" size="100%">Mathematics and Computers in Simulation</style></secondary-title><short-title><style face="normal" font="default" size="100%">Mathematics and Computers in Simulation</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Beetle antennae search</style></keyword><keyword><style  face="normal" font="default" size="100%">Finance</style></keyword><keyword><style  face="normal" font="default" size="100%">Neural networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Nonlinear programming</style></keyword><keyword><style  face="normal" font="default" size="100%">WASD neuronet</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/S0378475421004031</style></url></web-urls></urls><isbn><style face="normal" font="default" size="100%">0378-4754</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper introduces a 3-layer feed-forward neuronet model, trained by novel beetle antennae search weights-and-structure-determination (BASWASD) algorithm. On the one hand, the beetle antennae search (BAS) algorithm is a memetic meta-heuristic optimization algorithm capable of solving combinatorial optimization problems. On the other hand, neuronets trained by a weights-and-structure-determination (WASD) algorithm are known to resolve the shortcomings of traditional back-propagation neuronets, including slow speed of training and local minimum. Combining the BAS and WASD algorithms, a novel BASWASD algorithm is created for training neuronets, and a multi-input BASWASD neuronet (MI-BASWASDN) model is introduced. Using a power sigmoid activation function and while managing the model fitting and validation, the BASWASD algorithm finds the optimal weights and structure of the MI-BASWASDN. Four financial datasets, taken from the European Central Bank publications, validate and demonstrate the MI-BASWASDN model’s outstanding learning and predicting performance. Also included is a comparison of the MI-BASWASDN model to three other well-performing neural network models, as well as a MATLAB kit that is publicly available on GitHub to promote and support this research.</style></abstract></record></records></xml>