Proceduralism may be even more prevalent now, fueled by the broad success of algorithms and associated competitive pressures to adopt them; and by the regulatory push to validate or "attest to" models, whose results are then vested with unrealistic credibility.
The success of the algorithm is dependent on the initial guess of the optimization process, the employed algorithm, (genetic, modal, etc).
The training data used in machine learning is a huge part of the success of an algorithm's results, so without that data, it is nearly impossible to determine whether a particular algorithm is functioning as described.
These parameters are crucial for the success of the algorithm.
We give examples of the success of the algorithm and show convergence tests.
The success of the algorithm is demonstrated numerically, as are certain limitations.
Sohn's research is still developing, but with the early success of the algorithm to detect these subtle changes, AI may have an important role to come in the health industry.
The procedure of selection of poles of the controlled structure, which is critical for the success of the algorithm, is proposed in this paper.
Two benchmark problems are treated to illustrate the success of the algorithm: that of a punctured membrane and that of a cantilever beam in cross flow.
The simulation results obviously show the success of our algorithm.
