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Highly accurate surrogates are constructed at each iteration by minimizing an estimation of the leave-one-out error with the cross-entropy method.
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The optimal number of groups is then obtained by minimizing a balanced estimation of the expected test cost.
Parameters estimation is achieved by minimizing an objective function, which is a measure between the difference of the predicted model outputs and experimental measurements.
The method presented in the following includes the input estimation into the estimation process of the dynamical model parameters by minimizing an objective function containing all parameters simultaneously.
Results: This article presents a new approach which includes the input estimation into the estimation process of the dynamical model parameters by minimizing an objective function containing all parameters simultaneously.
One commonly used method is block-based motion estimation, where the current frame is divided into blocks, and each block is matched with a block in the last frame by minimizing an error function.
The proposed SRR algorithm is based on the stochastic regularization technique of Bayesian MAP estimation by minimizing a cost function.
The optimal location of the measurement sensors is computed by minimizing a cost function of the estimation error in the closed-loop infinite-dimensional system.
The parameter γ for the radial basis function (RBF) kernel, together with the regularization parameter C, and the loss function parameter ε were automatically chosen during SVR function estimation by minimizing a bound on the leave-one-out error.
After getting an optimal input, the parameter estimation is performed by minimizing a weighted least square criterion.
Parameter estimation is usually performed by minimizing a cost function which quantifies the differences between model predictions and measured data.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com