Sentence examples for imprecision in the model from inspiring English sources

By allowing for imprecision in the model, fuzzy logic opens the possibility for the inclusion of imprecise inputs and imprecise thresholds (Homayouni et al. 2009).

Therefore, a normal penalty stiffness in contact surfaces of the joint were introduced, and used as a calibration parameter to account for the simplifying assumptions or indeliberate imprecision in the model, e.g. concerning boundary conditions, material properties and geometrical detail.

Such softened model constraints may be used to roughly incorporate imprecision in the model arising, for instance, from non-compliance with the pseudo-steady-state assumption, partial unbalance of some metabolites or uncertain yields.

If the model did not fully explain all deaths prevented or postponed in each fifth, we assumed any shortfalls to reflect unmeasured risk factors or imprecision in the model's parameters.

With the variety of learning data now available, we have to accept that there is greater room for imprecision in the learner model.

These estimated net increases are somewhat higher than the observed increases in 5-year survival in CC and RC during the study period (ie, 7.7% and 7.1%, respectively), likely resulting from imprecision in the prediction model and additional unmeasured influences that had a small net negative impact on 5-year survival during the study period.

Fuzzy goal programming utilizes fuzzy set theory to deal with a level of imprecision in the goal programming model.

Inaccuracy or imprecision in the inputs propagate through the model to affect the accuracy and precision of the outputs.

The results of this comparison show that the fuzzy set approach is more suitable for evaluating imprecision in screening models than the classical technique.

We will conduct sensitivity analysis to take account of uncertainty and imprecision in the measurements, including multiple imputation models for missing values.

Handling of missing covariate data in PopPK analyses in an appropriate way, i.e., one that does not induce bias or imprecision in model parameter estimation is important, and several methods are available to the modeler.