This is the repo for the paper "Evolutionary optimization of convolutional ELM for remaining useful life prediction " which is an extension of its previous work specified in https://github.com/mohyunho/MOO_ELM
The objective of this study is to search for the best convolutional ELM, so-called conv ELM or CELM, architectures in terms of a trade-off between RUL prediction error and training time, the latter being determined by the number of trainable parameters, on the CMAPSS dataset.
you can find the trade-off solution by running the python codes below:
python3 enas_convELM_CMAPSS.py
Our experimental results on the CMAPSS dataset are shown as below: (a) FD001, (b) FD002, (c) FD003, and (d) FD004.
To cite this code use
@article{mo2023celm,
title={Evolutionary Optimization of Convolutional Extreme Learning Machine for Remaining Useful Life Prediction},
author={Mo, Hyunho and Iacca, Giovanni},
journal={SN Computer Science},
year={2023},
publisher={Springer},
note={to appear},
}
[1] Hyunho Mo and Giovanni Iacca. Evolutionary optimization of convolutional extreme learning machine for remaining useful life prediction. SN Computer Science, 2023. to appear.