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For nonlinear fitting, square sum of error (SSD) was applied.
Geometrical error sources are expressed as error twists, and the integrated model is calculated as the sum of error twists.
The tuning rules are developed for a minimum time domain integral performance index described by a weighted sum of error index and controller effort.
The adding up restriction of the factor shares leads to singularity because the sum of error terms is zero for each firm.
The resultant sum of error squares (SSEs) and coefficient of determination (r2) suggested that all selected isotherm models were applicable to represent the experimental data for the whole concentration range (10 50 mg L−1).
The validity of each model was determined by the sum of error squares (SSE %) given by % {text{SSE}} = sqrt {frac{{(q_{(exp )} - q_{{({text{cal}})}} )^{2} }}{N}} (6 where N is the number of data points.
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Ordinary or total least-squares methods for circle fitting seek to minimize the squares sum of error-of-fit with respect to measures [7, 8].
The approach seeks to solve a GLS problem that minimizes the sum of errors in traffic counts and O D matrices based on an equilibrium assignment mapping derived exogenously from a K-shortest path ranking procedure.
To achieve the best performance model, a scoring table was proposed considering several factors including coefficient of determination, sum of errors, difference between training and testing errors, sum of residuals, deviation of predicted results from experimental one and complexity and generality of the models.
But if η3B was the intended identity of η3, the error on y5 in Figure 2 would be the sum of error-5 and error-C from Figure 3.
For errors, the sum of errors over the blocks was computed.
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