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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.
In the case of full observability, an almost optimal filter is derived, where optimality refers to minimizing a worst-case estimation error.
Most packages share the concept of parameter estimation based on minimizing an objective function value (OFV), often using maximum likelihood estimation.
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 rate parameters were determined in a systematic way by estimation techniques minimizing a multi-response objective 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.
The design procedures are based on a set of convex optimization problems with linear matrix inequalities and result in the suboptimal choice of estimator gains which stabilize the estimation error dynamics and minimize a norm of the estimation error correlation matrix.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com