Functionality Matlab code

Versión 1.1.0 (46,5 KB) por vasanza
These functions facilitate preprocessing, feature extraction, feature selection, classification and regression of temporal signals.
115 descargas
Actualizado 30 nov 2021

View Functionality Matlab code on File Exchange

⭐⭐⭐⭐⭐ MATLAB Code

➡️ #Matlab #mat #ClassificationLearner #Classification #RegressionLearner #Regression

These are functions developed in matlab and used in the following applications:

We hope that all the functions in this repository will be useful to you in the programming of your Matlab projects.

When using this resource, please cite the original publication:

  • Estrada, R., Asanza, V., Torres, D., Bazurto, A., & Valeriano, I. (2022). Learning-based Energy Consumption Prediction. Procedia Computer Science, 203, 272-279, doi: https://doi.org/10.1016/j.procs.2022.07.035
  • V. Asanza, R. E. Pico, D. Torres, S. Santillan and J. Cadena, "FPGA Based Meteorological Monitoring Station," 2021 IEEE Sensors Applications Symposium (SAS), 2021, pp. 1-6, doi: 10.1109/SAS51076.2021.9530151.

Classification Learner

Classification

Related Work (Classification)

Regression Learner

Prediction

Related Work (Regression)

Datasets

Repository technical specifications

To work better it is recommended:

  • The main code in the project folder
  • Put dataset in a subfolder called "Data"
  • Put these functions in a subfolder called "src"
  • Use in main code: addpath(genpath('./src'))%functions folders

About

Keynote

Clone

Switched to Branch

  • git branch -a
  • git checkout NameBranch

New Branch

  • git checkout -b NameBranch

Push

  • git pull origin NameBranch
  • git status
  • git add .
  • git status
  • git commit -m "message"
  • git push origin NameBrach

Citar como

vasanza (2024). Functionality Matlab code (https://github.com/vasanza/Matlab_Code/releases/tag/1.1.0), GitHub. Recuperado .

Asanza Vı́ctor, et al. “SSVEP-EEG Signal Classification Based on Emotiv EPOC BCI and Raspberry Pi.” IFAC-PapersOnLine, vol. 54, no. 15, Elsevier BV, 2021, pp. 388–93, doi:10.1016/j.ifacol.2021.10.287.

Ver más estilos

Estrada, R., Asanza, V., Torres, D., Bazurto, A., & Valeriano, I. (2022). Learning-based Energy Consumption Prediction. Procedia Computer Science, 203, 272-279, doi: https://doi.org/10.1016/j.procs.2022.07.035.

J. Landívar, C. Ormaza, V. Asanza, V. Ojeda, J. C. Avilés and D. H. Peluffo-Ordóñez, "Trilateration-based Indoor Location using Supervised Learning Algorithms," 2022 International Conference on Applied Electronics (AE), 2022, pp. 1-6, doi: 10.1109/AE54730.2022.9920073.

Compatibilidad con la versión de MATLAB
Se creó con R2021b
Compatible con cualquier versión
Compatibilidad con las plataformas
Windows macOS Linux

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Versión Publicado Notas de la versión
1.1.0

Para consultar o notificar algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.
Para consultar o notificar algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.