Evolutionary optimization of convolutional ELM for remaining useful life prediction

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

MOO CELM

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},
}

References

[1] Hyunho Mo and Giovanni Iacca. Evolutionary optimization of convolutional extreme learning machine for remaining useful life prediction. SN Computer Science, 2023. to appear.