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A genetic algorithm is applied to permit multi-objective optimization, and optimal values are obtained for the design parameters (including solid oxide fuel cell inlet temperature, fuel utilization factor, current density and steam-to-carbon ratio).
Of course, the values of the parameters are not known a priori, and even when optimally computed, optimal values are subject to uncertainty depending on the type of experiments and the presence of experimental noise.
where α > 0 is called the regularization parameter whose optimal values are determined through optimization for minimum estimation errors.
Tollbäck et al. [ 25] published a review on optimization parameters for PTV injection and their optimal values are reported in Table 1.
Optimal values are reported in bold.
Optimal values are highlighted in bold.
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The theoretical optimal values were in agreement with experimental observations.
Eventually, the confirmation experiments are carried out to demonstrate the optimal values were effective and reliable.
These optimal values were much higher than those of mixed Cu–W composites.
Grey Fuzzy Reasoning Grade (GFRG) is developed and the optimal values were found out from the Grey relational grade.
Different parameter values for calculating texture features were tested, the optimal values were selected by maximizing the AUC result.
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CEO of Professional Science Editing for Scientists @ prosciediting.com