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A traditional BPN is normally trained by minimizing the absolute difference between target outputs and approximate outputs.
The adaptation mechanism is used to update the related parameters of the model while minimizing the absolute value of the instantaneous error between the system and model outputs.
Chen and Ramli [15] propose to select the threshold minimizing the absolute mean brightness error between the input and the resulting images.
Among different possibilities to start the next pathway [20], we select the index minimizing the absolute difference of the function values.
Instead, we employ a relaxation based on an L1 metric (Shlomi et al., 2005), minimizing the absolute value of fluxes rate through transport reactions (which requires no additional Boolean variables).
The smoothing parameter for time was chosen by minimizing the absolute value of the sum of partial autocorrelation (PAC) of the residuals; however, a minimum of 1 degree of freedom per year was required.
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ANN traditionally minimizes the absolute difference between target outputs and approximate outputs, thereby resulting in approximate optimal solutions being sometimes actually infeasible when it is used as a meta-model for inequality constraint functions.
The optimal value of the cross-year autocorrelation (rho ^_{k}) is the (rho _{k}) which minimizes the absolute distance between (hat{lambda }^) and any (hat{lambda }_{k}).
Having determined one point of the image as an element of the path vector p, we seek the neighborhood of this point for the neighbor that minimizes the absolute difference |f(p−f(p(ℓ + 1))| of the function values.
Hence, the last equation of (30), (32) and (34) constitute a constrained programming problem of a quadratic curve on a plane, and the optimization objective is to minimize the absolute value of V bo1 + V bo2.
We then chose the smoothing parameters that minimized the absolute value of the sum of partial autocorrelations (PACs) of the residuals from lags 3 to 30 days.
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