Sentence examples for a fitting error from inspiring English sources

In this paper, we propose a fitting error interpolation based library search (FEI-LS) method to improve the measurement accuracy.

The correlation coefficient (R) was greater than 0.99, corresponding to a fitting error of less than ±10.5% based on a 95% confidence level.

Experimental results revealed a monotonous power function relationship between the gas flow and the Bragg wavelength with a fitting error less than 3% and a gas flow detection limit of ∼0.178 m3/h in a range up to 32 m32h.

For robustness of results, only data with a fitting error <20% of the standard deviation was included in the final analysis.

According to Fig.  8, we have 50%% to have a fitting error \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$E\le 0.040$$\end{document} E ≤ 0.040.

Note that for instance the L=10 curve shows no resemblance with the Gaussian metric curve and thus yields a high fitting error.

myo-inositol to Cr ratios from the striatum, cerebellum and occipital cortex of one non-prion patient and from the striatum of one control were also excluded from the analysis due to an estimated fitting error >20% for myo-inositol.

The model is then identified by seeking coefficients of the combination that minimize a mixed objective, composed by a term representing the fitting error and a term inducing sparsity in the representation, which results in a problem formulation of the "square-root LASSO" type, with nonnegativity constraints on the variables.

However, as shown in Figure 3a, with this number of compartments, a large discrepancy is observed between the data and best model fit (total fitting error 5.68).

The exclusion criterion for metabolite evaluation was an LCModel estimated fitting error >20%, this being a reliable indicator of poor quality spectra.

These results showed that in the ANN training with the FIB-GA, no parameter set correlated with the fitting error, a finding that implies there is no dominant parameter in ANNs trained via the use of an FIB-GA.