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A search strategy based on mathematical programming is developed to minimize the upper bound for Pf.
The weighting matrix of the residual norm is designed to minimize the upper bound of the estimation error variance.
The MPC problem is formulated to minimize the upper bound of infinite horizon cost that satisfies the sufficient conditions.
Then, an optimal robust guaranteed cost controller is developed to minimize the upper bound of performance level.
The SRM principle seeks to minimize the upper bound of the generalization error consisting of the sum of the training error and a confidence interval.
The objective is to minimize the upper bound of an infinite horizon cost function subject to a terminal inequality by using induced L2-norm bound.
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Motivated by the above observations, we consider that the different links might have different fading gain with heterogeneous path loss in this subsection to minimize the upper-bound of sum leakage interference and improve the throughput for each receiver.
In SVR, the structural risk minimization is implemented for minimizing the upper bound of generalization error, while ANN takes the advantage of empirical risk minimization to minimize the prediction error (Asoodeh and Bagheripour 2013).
The extended Kalman filter is derived through minimizing the upper bound for the filtering error covariance.
The design condition realizes guaranteed cost control by minimizing the upper bound of a given performance function.
Furthermore, a convex optimization problem with LMI constraints is formulated to design the optimal guaranteed cost controller which minimizes the upper bound of the closed-loop cost function.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com