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These techniques minimize a cost function by exploring an application parameter search space.
They are formulated as optimization problems, seeking values for the model's inputs that minimize a cost function measuring departures from steady state and from date.
The task is to determine the optimal settings of the production parameters to minimize a cost function.
Traditionally for these problems, the objective function is to minimize a cost function while satisfying the reliability constraints.
First, a full factorial design of experiments allowed estimation of a set of parameters of the numerical model that minimize a cost function.
We introduce a novel approach to controlling the motion of a team of agents so that they jointly minimize a cost function utilizing Bayes risk.
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Predictive control algorithms compute the manipulated variable minimizing a cost function considering expected future errors.
The parameters in the fuzzy mechanism are tuned by minimizing a cost function.
The model-based inverse problem is solved by minimizing a cost functional using genetic algorithms.
The source current density was obtained through an inverse problem by minimizing a cost function including a data misfit contribution and a regularizer.
Further, the problem of designing a robust controller is translated into an optimal control problem minimizing a cost function.
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