Online-Recurrent-Extreme-Learning-Machine
Online-Recurrent-Extreme-Learning-Machine (OR-ELM) for time-series prediction, implemented in python.
Requirements
- Python 2.7
- Numpy
- Matplotlib
- pandas
- Expsuite (included in this repository)
Dataset
Implemented Algorithms
- Online Sequential Extreme Learning Machine (OS-ELM)
- Liang, Nan-Ying, et al. "A fast and accurate online sequential learning algorithm for feedforward networks." IEEE Transactions on neural networks 17.6 (2006): 1411-1423.
- Fully Online Sequential Extreme Learning Machine (FOS-ELM)
- Wong, Pak Kin, et al. "Adaptive control using fully online sequential-extreme learning machine and a case study on engine air-fuel ratio regulation." Mathematical Problems in Engineering 2014 (2014).
- Normalized FOS-ELM (NFOS-ELM) (proposed)
- FOS-ELM + Layer Normalization + forgetting factor
- Normalized Auto-encoded FOS-ELM (NAOS-ELM) (proposed)
- FOS-ELM + Layer Normalization + forgetting factor + weight auto-encoding (input->hidden)
- Online Recurrent Extreme Learning Machine (OR-ELM) (proposed)
- FOS-ELM + Layer Normalization + forgetting factor + weight auto-encoding (input->hidden, hidden->hidden)
- This is for training recurrent neural networks (RNNs)
Example of usage
Run prediction code:
python run.py -a ORELM
Plot performance comparison:
python plotResults.py
Result
- Performance comparison
- FOS-ELM and proposed variants including OR-ELM
To do
- Rewrite this code with Pytorch for GPU acceleration
If you use this code, please cite our paper "Online Recurrent Extreme Learning Machine and its Application to time-series Prediction" in IEEE Access.
Paper URL: http://ieeexplore.ieee.org/abstract/document/7966094/ http://rit.kaist.ac.kr/home/International_Conference?action=AttachFile&do=get&target=paper_0411.pdf
Park, Jin-Man, and Jong-Hwan Kim. "Online recurrent extreme learning machine and its application to time-series prediction." Neural Networks (IJCNN), 2017 International Joint Conference on. IEEE, 2017.
Acknowledgement
This work was supported by the ICT R&D program
of MSIP/IITP. [2016-0-00563, Research on Adaptive Machine
Learning Technology Development for Intelligent Autonomous
Digital Companion]