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Wang et al. [13 15] analyzed the optimization of parameters for bonnet polishing based on the minimum residual error method.
Its closed-loop configuration converges close to the minimum residual error using feedback from the fullband error signal, but its stability and convergence rate are limited by the error path delay.
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The minimum Prediction Residual Error Sum of Squares (PRESS) was 0.824 tested by leave one out cross validation.
The motion model decision algorithm selects, for each SI block, the best motion model (translational or perspective) using a MAD criterion, i.e., the motion model with the minimum MAD residual error for a given SI block is chosen.
Here the estimated parameter values of ζ1 = 0.0935 ± 0.003, ζ2 = 0.31 ± 0.01, and ζ3 = 1.61 ± 0.05 resulted in the minimum weighted residual error between simulated and model-estimated F S F sim values.
The model with the least median (minimum median residual errors) is chosen.
Finally, we repeat the above mentioned steps 1 5 on all 56 different BSIF filters in the bank and obtain the final comparison score that corresponds to the minimum of residual errors obtained on all 56 filters in the bank.
It can be seen from Table 2 that the minimum of average residual error for (theta) attains at (h_2=-0.93 h_2=-0.93
Finally, we select the best comparison score that corresponds to the minimum value of the residual error.
Liao [25], Yabushita et al. [26] and Abbasbandy et al. [27] applied the homotopy analysis method to nonlinear ODE's and suggested the so-called optimization method to find out the optimal convergence control parameters by minimum of the square residual error integrated in the whole region having physical meaning.
Models with either five or three relevant context positions in addition to the stop codon correspond to the local and global residual error minimum, respectively.
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