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When compared with voltage sensitivity index method, WOA and index vector methods gives better results.
We show that the present family of methods gives better approximations and generalize all the existing finite difference and spline functions based methods up to order six.
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The present methods give better control performances.
It is shown that neuro-fuzzy methods give better results than other, more conventional, modeling approaches.
We show that neuro-fuzzy methods give better predictions than conventional modelling approaches.
The comparison shows that both probabilistic methods give better predictions than the "standard" model.
Therefore, the present methods give better prediction results than traditional methods that are based on exact solutions.
As can be seen, all the adopted weighted averaging methods give better results compared to the simple averaging (Sum) rule.
However, for the validation problem, both fusion methods give better false acceptance rate/false rejection rate values regarding to individual biometrics.
However, when it is interpreted as a validation problem, both fusion methods gave better FAR/FRR values regarding to individual biometrics.
All three SS methods give better results than the baseline for Test Sets A and B, while the improvement for Test Set C is relatively insignificant.
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