@proceedings {7236,
	title = {Dynamic Adaptation for Length Changeable Weighted Extreme Learning Machine},
	journal = {International conferance of intelligent},
	year = {2020},
	address = {Amsterdam, The Netherland},
	abstract = {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.},
	url = {https://www.researchgate.net/publication/337945405_Dynamic_Adaptation_for_Length_Changeable_Weighted_Extreme_Learning_Machine},
	author = {Berghout, Tarek and Mouss, Leila-Hayet and Ouahab KADRI}
}
