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Adding noise to the simulated signal checks the robustness of the algorithm against measurement noise.
The robustness of the algorithm is also investigated with respect to the load of the refrigerator.
Thus the robustness of the algorithm grows with the size of the network.
Extensive Monte Carlo simulations have been performed to test the robustness of the algorithm.
In addition to this, an in-depth evaluation of the robustness of the algorithm is presented.
Moreover, the results of PRO have less cost function which is an indication of the robustness of the algorithm.
At last, some numerical experiments are given to show the application of the models and the robustness of the algorithm.
Finally, the robustness of the algorithm to changes in current, pressure, temperature, and humidity operating conditions is examined.
A regularization based on a viscous crack resistance that even enhances the robustness of the algorithm may easily be added.
Furthermore, the robustness of the algorithm was verified by performing experiments with target functions perturbed with various levels of additive noise.
Numerical simulations on discrete and continuous structural systems demonstrated the robustness of the algorithm in detecting the locations and extent of small to large levels of damage.
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