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.
引用格式
Ayad Al-Rumaithi (2024). System Identification using ANN (https://www.mathworks.com/matlabcentral/fileexchange/72094-system-identification-using-ann), MATLAB Central File Exchange. 检索来源 .
MATLAB 版本兼容性
平台兼容性
Windows macOS Linux类别
- AI, Data Science, and Statistics > Deep Learning Toolbox > Sequence and Numeric Feature Data Workflows >
标签
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!