Sentence examples for relative root mean square from inspiring English sources

The relative root mean square errors of the model for growth ranged between 0.09 and 0.31.

Simulation results indicate that the proposed methods perform competitively in terms of relative bias and relative root mean square error.

The correlation coefficient between the observed thicknesses in dataset 1 and the modelling results was 0.76 (Fig. 3e), with a relative root mean square error (RMSE) of 0.10.

The performance of all models was assessed two common statistical parameters: the relative root mean square error (RMSE) and the relative mean bias error (MBE).

Also, the proposed model showed the lowest mean bias error (MBE) and relative root mean square error (RMSE) compared to the other models.

Validation of the models with the independent data indicated that all relative root mean square errors (RRMSEs) between the measured and simulated values were below 20%.

In more than 78% of the cases, comparison of simulations and measurements of the indoor climate yielded a relative root mean square error of less than 10%.

Model performances were satisfactory, with average relative root mean square error and modelling efficiency for aboveground biomass simulation of 34.33% and 0.57, respectively.

Using three statistical parameters (coefficient of determination, relative root mean square error, and model efficiency), critical examinations were made on the accuracy of these models.

The performance of all models was assessed with two common statistical parameters: the relative root mean square error (RMSE) and the relative mean bias error (MBE).

The results demonstrate that by applying this calibration method the relative root mean square (RMS) error of the NRLMSISE00 model can be reduced by about 9%.