Extreme Learning Machine Toolbox

This is a toolbox to enable the creation of extreme learning machines in MATLAB. It is possible to create a desired model using just a few lines of code. This version also includes the possibility to add functional link between the inputs and output.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

You just need MATLAB. This toolbox has been developed on MATLAB R2019a, but should also be compatible with earlier versions.

Installing

Toolbox installation in MATLAB is rather simple. You just need to add a copy of the source codes (src directory) in the MATLAB paths.

This can be made programatically with the following code:

addpath('<directory>/ELM')

Two simple demos can be found in the examples directory. To run them, you just need to execute the scripts Demo_classification.m and Demo_regression.m.

Deployment

Add additional notes about how to deploy this on a live system

Built With

• MATLAB R2019a

Contributing

Please contact the author (victor.henrique@pucpr.edu.br).

Versioning

We use a three-codes versioning system: vA.B.C.

• A stands for the major revision, and will be used when a major new feature is added or compatibility to previous major revision is lost.
• B stands for minor revision, where new features will be added.
• C is used for minor adjustments and bug fixing.

Authors

• Victor Henrique Alves Ribeiro, MSc - Coding - Industrial and Systems Engineering Graduate Program, Pontifícia Universidade Católica do Paraná - Brazil
• Gilberto Reynoso-Meza, PhD - Supervision - Industrial and Systems Engineering Graduate Program, Pontifícia Universidade Católica do Paraná - Brazil
• Hugo Siqueira Valadares, PhD - Supervision - Computer Science Graduate Program, Electrical Engineering Graduate Program, Federal University of Technology - Paraná - Brazil

Also, this work has been built based on code from Dr. Levy Boccato (UNICAMP- Brazil), Mr. Qin-Yu Zhu, and Dr. Guang-Bin Huang (Nanyang Technological University - Singapore).

License

This project is licensed under the 3-clause BSD License - see the LICENSE.txt file for details.

Citation

If this toolbox is of any help, please cite our work

@article{ribeiro2020multi,
  title={Multi-objective ensembles of echo state networks and extreme learning machines for streamflow series forecasting},
  author={Ribeiro, Victor Henrique Alves and Reynoso-Meza, Gilberto and Siqueira, Hugo Valadares},
  journal={Engineering Applications of Artificial Intelligence},
  volume={95},
  pages={103910},
  year={2020},
  publisher={Elsevier}
}

Please, also cite the works for Extreme Learning Machines and Random Vector Functional Link:

@article{huang2006extreme,
  title={Extreme learning machine: theory and applications},
  author={Huang, Guang-Bin and Zhu, Qin-Yu and Siew, Chee-Kheong},
  journal={Neurocomputing},
  volume={70},
  number={1-3},
  pages={489--501},
  year={2006},
  publisher={Elsevier}
}

@incollection{husmeier1999random,
  title={Random vector functional link (RVFL) networks},
  author={Husmeier, Dirk},
  booktitle={Neural Networks for Conditional Probability Estimation},
  pages={87--97},
  year={1999},
  publisher={Springer}
}