RUL prediction (C-MAPSS dataset)

Dynamic Adaptation for Length Changeable Weighted Extreme Learning Machine

This work introduces a new improvements in LCI-ELM proposed in [1]. 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[2]. PSO[3] and R-ELM[4] 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

[1] 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.
[2] A. Saxena, M. Ieee, K. Goebel, D. Simon, and N. Eklund, “Damage Propagation Modeling for Aircraft Engine Prognostics,” Response, 2008.
[3] M. N. Alam, “Codes in MATLAB for Particle Swarm Optimization Codes in MATLAB for Particle Swarm Optimization,” no. March, 2016.
[4] 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.