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Figure 13 Mean error between the predictions and the measured RTT for the stationary network.
The quality of the neural network model was evaluated by means of the Cross validation technique, with a mean error between δexp and δcalc of 0.60 ppm.
Model curves for 1D diffusion kinetics were calculated by minimizing the unsigned mean error between the experimental data and the model.
The maximal mean error between fingertip measured position and fingertip position obtained from simulated joint angles and forward kinematics is 0.99±0.76 mm for the training set and 1.49±1.62 mm for the test set.
The UDCP optimizer implements an iterative process to minimize the mean error between the user-defined target L T and the estimated aggregate controlled load L C in each switching cycle of a switching program.
As a result of the gradual variance, the mean error between the predicted score for time t (calculated at t - 1 s), and the actual aggregate score observed at time t is 9.85%.
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The mean errors between given and simulation-predicted time-courses were 1.6 × 10−5% and 2.0 × 10−3%, respectively.
The predictive capability of the multivariate method developed by partial least squares regression was satisfactory, allowing mean errors between 0.33 and 10% in the simultaneous determination of ATZ, DIA and DEA in aqueous solution.
One-way ANOVAs were used to test for significant differences in mean and absolute mean errors between measured and self-reported values based on specific characteristics.
Step 3. In the third step, we analyze the normalized mean error ranges between each pair of nodes.
We computed the angular mean error (AME) between the directions θ of the mixing matrix A and its estimation.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com