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This study has proposed an approach for reducing parameter and structural uncertainty in LCC prediction modeling.
Simulation results demonstrate the function of OED in reducing parameter estimation errors.
Based on possibilistic approach, this study proposes an approach for reducing parameter and structural uncertainty in LCC prediction modeling.
Numerical studies demonstrate the efficiency of this optimization strategy and show the effectiveness of this integrated OED in reducing parameter estimation uncertainties.
Numerical results demonstrate the effectiveness of this iterative double-layer OED optimization strategy in reducing parameter estimation uncertainties compared with conventional approaches.
The aim of this section is to validate and to evaluate the performance of the proposed approach through two case studies for reducing parameter and structural uncertainty in LCC prediction modeling.
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Fitting the data to eos yielded the characteristic reducing parameters, viz.
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Drug administration that was begun 1 hour after disease induction was highly effective in reducing parameters of pathobiologic response, significantly more so than when begun 6 hours after disease induction, in all models.
Reduced parameter.
To consider a reduced parameter space, we take the prevalence α a as given.
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