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Minimum absolute percent relative error = ({ hbox{min} }left| {text{Er}} right|).
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The minimum and maximum absolute percent errors are also shown.
In this article, the optimal weighted value of each subnet is determined according to the minimum predicted absolute percent error (APE) of x ^ i t + 1 in each case.
Six statistical indicators (e.g., percent relative error, maximum absolute percent relative error, average absolute percent relative errors, percent bias, index of agreement, and Nash Sutcliffe efficiency) and relative computer computation time are used for assessing the model performance.
The proposed neural network models showed high accuracy in predicting the P b, B ob, and µ ob values, and achieved the lowest relative error, lowest absolute percent relative error, lowest minimum error, lowest maximum error, and lowest standard deviation of relative error.
Mean absolute percent error.
Average absolute percent relative error.
Minimum absolute per cent error.
The overall average absolute percent error is 2.4%.
The average absolute percent differences vary between 0 30%.
On average, absolute percent disparity varies between about 0 10%.
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