Functionality Matlab code

バージョン 1.1.0 (46.5 KB) 作成者: vasanza
These functions facilitate preprocessing, feature extraction, feature selection, classification and regression of temporal signals.
ダウンロード: 131
更新 2021/11/30

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

引用

vasanza (2026). Functionality Matlab code (https://github.com/vasanza/Matlab_Code/releases/tag/1.1.0), GitHub. 取得日: .

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.

その他のスタイルを見る

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.

MATLAB リリースの互換性
作成: R2021b
すべてのリリースと互換性あり
プラットフォームの互換性
Windows macOS Linux
バージョン 公開済み リリース ノート
1.1.0

この GitHub アドオンでの問題を表示または報告するには、GitHub リポジトリにアクセスしてください。
この GitHub アドオンでの問題を表示または報告するには、GitHub リポジトリにアクセスしてください。