Nonlinear System Identification using ANN

版本 1.0.5 (9.0 KB) 作者: Ayad Al-Rumaithi
Nonlinear system identification using artificial neural network example
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This example file (Example.m) performs system identification using artificial neural network (ANN) on the nonlinear dynamic behaviour of a two dimensional frame. The output of the two dimensional frame was generated using Two Dimensional Frame Nonlinear Dynamic Solver:
https://www.mathworks.com/matlabcentral/fileexchange/111825-two-dimensional-frame-nonlinear-dynamic-solver
The fourteen necessary files of that solver are already uploaded in this submission.
The neural network consist of the following layers:
-Input layer: 1 node for the force at the current step, 4 nodes for the displacement at the previous four steps, and 4 nodes for the base shear at the previous four steps using open-loop feedback.
-Hidden layer: 4 nodes for four inner states.
-Output layer: 1 node for the displacement and 1 node for the base shear.
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 two figures.

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Ayad Al-Rumaithi (2024). Nonlinear System Identification using ANN (https://www.mathworks.com/matlabcentral/fileexchange/131873-nonlinear-system-identification-using-ann), MATLAB Central File Exchange. 检索来源 .

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