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The filtered dictionary F = {f1,..., f L } is updated every 10-30 ms. The dictionary vectors and gains are chosen to minimize the norm of the error vector.
The second problem is to minimize the norm of the control operator, over all such subsets.
The first problem is to determine the optimal shape and position of ω in order to minimize the norm of the control for given initial data, over all possible measurable subsets ω of [0,π] of Lebesgue measure Lπ.
Specifically, we minimize the norm (11 where g j represents the gravity change at the j-th point and (u i, v i, w i ) eastward, northward and upward displacement components at the i-th point, respectively.
The parameters of the model for calibration and correction of step-corrected accelerations are estimated by a least-squares fit against non-gravitational accelerations from POD, i.e., we minimize the norm ( vert vert a_mathrm{pod} - a_mathrm{cal} vert vert ).
In [13], Huang has discussed the similarities between RELM and SVM and proven that to minimize the norm of the output weight ∥β∥2 is actually to maximize the distance of the separating margins of two different classes in the ELM feature space, which is similar to SVM.
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These coefficients are predicted by minimizing the norm of the residual vector using genetic algorithms.
The significant reduction in the long-wavelength phase slopes might be caused by minimizing the norm of the solutions.
We can then get an estimate of the target's position by minimizing the norm of (10).
We propose a solution that allows a trade-off between minimizing the norm of the solution and the approximation error involved in the projection.
In this paper, the predictive PID gains are obtained by minimizing the norm difference between control signal obtained from Generalized Predictive Control (GPC) and predictive PID formulation.
More suggestions(15)
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com