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The introduction of imperfections reduced the percentage errors between computations and tests within 5% in so far as buckling and collapse loads are concerned.
To address this issue, the relative percentage errors between the corresponding constants obtained using the practical data and the calculated model based constant are computed only using the second IMF to the sixth IMF.
The percentage errors between the actual ranges and ranges with offset are recorded.
The percentage errors between the scatter-corrected count and the primary count for the planar images were evaluated.
Table 3 shows the absolute percentage errors between the scatter corrected and the primary counts for the simulated planar images.
So, corrected annual capacity factor of wind turbine are calculated and then the percentage errors between the measured and corrected values of annual capacity factor are obtained as shown in Table 10.
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The three architectures produced a mean absolute percentage error between 4 and 6%, but the feedforward architecture behaved better considering validation and test sets, with 4,2% error.
Finally, the construction parameters of the mechanism are determined and the variation of the percentage error between the desired and generated function values is obtained.
The percentage error between the solid and liquid interface energies predicted from ΔT∗ and Δ T∗∗ is smaller than 0.59% when the temperature is in the range 783 1683 K.
The test results showed that the coefficient of determination and mean absolute percentage error between the optimized artificial neural network estimations and measured values for testing datasets are higher than 99% and %5, respectively.
The percentage error between predicted and experimental results for the removal of bentazon, carbofuran and 2,4-D were 8.2, 1.3 and 9.2%, respectively and for the yield of the palm oil frond activated carbon was 5.6.
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