This work introduces a new improvements in LCI-ELM proposed in . The new contributions focus on the adaptation of training model towards higher dimensional “time –varying “data. The proposed Algorithm is investigated using C-MAPSS dataset. PSO and R-ELM training rules are integrated together for this mission.
The details of the proposed Algorithm and the user guide are available in : https://www.researchgate.net/publication/337945405_Dynamic_Adaptation_for_Length_Changeable_Weighted_Extreme_Learning_Machine
 Y. X. Wu, D. Liu, and H. Jiang, “Length-Changeable Incremental Extreme Learning Machine,” J. Comput. Sci. Technol., vol. 32, no. 3, pp. 630–643, 2017.
 A. Saxena, M. Ieee, K. Goebel, D. Simon, and N. Eklund, “Damage Propagation Modeling for Aircraft Engine Prognostics,” Response, 2008.
 M. N. Alam, “Codes in MATLAB for Particle Swarm Optimization Codes in MATLAB for Particle Swarm Optimization,” no. March, 2016.
 J. Cao, K. Zhang, M. Luo, C. Yin, and X. Lai, “Extreme learning machine and adaptive sparse representation for image classification,” Neural Networks, vol. 81, no. 61773019, pp. 91–102, 2016.
BERGHOUT Tarek,Mouss Leila Hayet, Kadri Ouahab, "Dynamic Adaptation for Length Changeable Weighted Extreme Lerning Machine", (https://www.mathworks.com/matlabcentral/fileexchange/<...>), MATLAB Central File Exchange. Retrieved December 9, 2019.
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