System identification using artificial neural network example
Vous suivez désormais cette soumission
- Les mises à jour seront visibles dans votre flux de contenu suivi
- Selon vos préférences en matière de communication il est possible que vous receviez des e-mails
This example file shows system identification using artificial neural network (ANN) of 2DOF system subjected to Gaussian white noise. The neural network consist of the following layers:
-Input layer: 2 nodes for the force at the current step and 2 nodes for the displacement at the previous step using open-loop feedback
-Hidden layer: 2 nodes for two inner states because there are 2 modes for 2DOF system
-Output layer: 2 nodes for the displacement
After training and getting the predicted output, the network was converted to closed-loop network and trained again (closed-loop networks uses predicted feedback from previous step instead of actual feedback). The predicted output from open-loop and closed-loop networks was compared with the actual output in a figure. It shows open-loop network is more accurate than closed-loop network due to the availability of actual output from the previous step.
Citation pour cette source
Ayad Al-Rumaithi (2026). System Identification using ANN (https://fr.mathworks.com/matlabcentral/fileexchange/72094-system-identification-using-ann), MATLAB Central File Exchange. Extrait(e) le .
Informations générales
- Version 1.0.6 (2,23 ko)
Compatibilité avec les versions de MATLAB
- Compatible avec toutes les versions
Plateformes compatibles
- Windows
- macOS
- Linux
| Version | Publié le | Notes de version | Action |
|---|---|---|---|
| 1.0.6 | Added website |
||
| 1.0.5 | description |
||
| 1.0.4 | description |
||
| 1.0.3 | figure |
||
| 1.0.2 | Added closed-loop network |
||
| 1.0.1 | file comments |
||
| 1.0.0 |
