Sentence examples for mean relative errors from inspiring English sources

It was determined that the predictions usually agreed well with the experimental results with correlation coefficients in the range of 0.940 1.000 and mean relative errors in the range of 1.40 27.40%.

It was found that the predictions usually agreed well with the experimental data with correlation coefficients in the range of 0.807 0.999 and mean relative errors in the range of 0.83 6.24%.

The predictions yielded good agreement with the experimental values with correlation coefficients in the range of 0.9906 0.9986, mean relative errors in the range of 1.39 2.28% and very low root mean square errors.

Simulation results showed that the developed model is a viable planning and analytical tool for aiding future off-grid PV-to-diesel system integration applications, with R2 values ranging from 0.92 to 0.99 and mean relative errors below 5%.

These predicted operating parameters by ANN model agreed well with the experimental values with correlation coefficient in the range of 0.962 0.998, mean relative errors in the range of 2.79 7.36% as well as root mean square error (RMSE) 10.59 kg h−1 and 12 K for refrigerant mass flow rate and refrigerant discharge temperature, respectively.

The mean relative errors are indicated as the red filled symbols.

Five measures were adopted to characterise the error of approximation: coefficient of correlation, mean relative error, RRMSE, EF, and CRM.

To compare between the models their root mean square error (RMSE), mean relative error (MRE) and mean absolute error (MAE) was found out.

A group of statistical measures have been used to evaluate the goodness of fit of these models, including root mean square error (RMSE), coefficient of determination (CD), the Nash coefficient (NA), mean relative error (MRE), mean symmetry error (MSE), percentage of data with a relative error ≤ 50%and25%5% (P50, P25), and percentage of data with overestimated error (POE).

The efficiencies of parameter estimation and modeling performance were calculated based on least square error (O(p)), mean relative error (MRE and Akaike Information Criterion AICIC).

21 22 Studies compared the forecasts to observed values using various measures: mean-squared error, mean relative error, mean percentage error, correlation coefficients, paired t tests (between predicted and observed values), 95% CI (of predicted values and determined if observed values fell within the interval) and visualisations (eg graphical representations of observed and predicted values).