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Parameters required for the electrochemical model are adjusted to reproduce experimentally observed data.
The parameters of fuzzy identifier model are adjusted online by using recursive least square algorithm.
The parameters of the ANN model are adjusted by the back propagation learning algorithm using wide ranges of experimental datasets.
In this method, outputs from the individual-tree model are adjusted such that the resulting stand summary matches prediction from a whole-stand model.
An important application of synchronization is in adaptive parameter estimation methods where parameters in a model are adjusted dynamically in order to minimize the synchronization error [13 15].
Some parameters of the numerical model are adjusted using cyclic and hybrid testing results on a 15 ton MR damper with internal coils.
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The results were analyzed and the numerical model was adjusted.
The model was adjusted using backwards elimination.
This model was adjusted for several potential confounders.
The basic model was adjusted for age.
The model was adjusted through backward elimination.
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