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Jovanovic and Harker [27] modeled the problem to minimize the deviation between actual and planned schedules.
The model updating is performed for the elastic parameters and the boundary conditions to minimize the deviation between experimentally determined and numerically calculated results in terms of eigenfrequencies.
(2) Random and tiny movement of a single particle to minimize the deviation between the obtained structural data and the expected one.
Two objectives are considered in structural optimization: one is to minimize total weight of the structure and the other is to minimize the deviation between the equivalent stress and the reference stress.
Common to all feedback control systems is the comparison of the sensor signal to a reference signal, and the existence of a controller that influences the system to minimize the deviation between the sensor and reference signals.
To solve this problem, intuitively, one can obtain the expression of the solution to equations (3) and (4) and then evaluate the unknown boundary conditions to minimize the deviation between the solution and the actual observed values.
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Based on the comparison results, a simulation-based optimization is performed with the objective to minimize the deviations between the planned and actual schedule.
The objective of the present paper is to study whether the conventional approach is acceptable for the simulation of the parameters of the power station, and to identify areas where the results of the simulation may be improved, in order to minimize the deviations between the simulated parameters and the actual measurements.
New model parameters are estimated by minimizing the deviation between the synthetic data and the model.
This objective is accomplished by minimizing the deviation between the designed surface and the actual surface during machining.
Multi-dimensional scaling (MDS), for example, calculates coordinates for a 2-dimensional embedding of the experimental data by minimizing the deviation between embedded and original data (Kruskal stress).
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