<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Berghout, Tarek</style></author><author><style face="normal" font="default" size="100%">Mouss, Leila-Hayet</style></author><author><style face="normal" font="default" size="100%">Ouahab KADRI</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Dynamic Adaptation for Length Changeable Weighted Extreme Learning Machine</style></title><secondary-title><style face="normal" font="default" size="100%">International conferance of intelligent</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.researchgate.net/publication/337945405_Dynamic_Adaptation_for_Length_Changeable_Weighted_Extreme_Learning_Machine</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Amsterdam, The Netherland</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper, a new length changeable extreme learning machine is proposed. The aim of the proposed method is to improve the learning performances of a Single hidden layer feedforward neural network (SLFN) under rich dynamic imbalanced data. Particle Swarm Optimization (PSO) is involved for hyper-parameters tuning and updating during incremental learning. The algorithm is evaluated using a subset from C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset of gas turbine engine and compared to its derivatives. The results prove that the new algorithm has a better learning attitude. The toolbox that contains the developed algorithms of this comparative study is publicly available.</style></abstract></record></records></xml>