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The proposed neural network system presents a maximum error lower than 15%, while existing design formulae errors are over 20%.
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The neural network presented a maximum error value lower than 30%, while existing formulas presented errors greater than 40%.
In all cases, the proposed model has outperformed other common prediction methods resulting in a lower maximum error as well as lower average absolute relative error making it more attractive than other correlations as observed in Table 5.
The maximum error measured was lower than 5 m and this was reduced to a level lower than 0.5 m.
Models with lower mean, median, and maximum error perform better than those with higher values.
While there existed a tolerance between both systems in estimation of the lower back compressive force, the maximum error percentage (≈10%) is considered within a reasonable range.
In this case, the maximum error of estimation ~8% was obtained when using 800ps gates for the lower values of scattering.
The most simplified skeletal mechanisms show an error in the prediction of temperature and fuel profiles lower than 3%, except for the case of low temperature and lean mixtures, where the maximum error increases up to 14%.
The maximum error obtained was 2.3%.
A board official added that the maximum error was 450 points, not 400.
Well, it finally got to the point where the maximum error, not the performance, but the maximum error went below a threshold that I had previously determined.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com