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System Identification using ANN

version 1.0.5 (2.15 KB) by Ayad Al-Rumaithi
System identification using artificial neural network example


Updated 09 Jul 2019

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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.

Cite As

Ayad Al-Rumaithi (2021). System Identification using ANN (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2017b
Compatible with any release
Platform Compatibility
Windows macOS Linux

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