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The fundamental of algorithm is the mean of error upper bound.

Zero valued relative error (i.e., mean of error distribution) and near unity value of correlation coefficient between measured and predicted oil FVF data confirms accomplishment of fuzzy modeling.

The statistical tests showed that most of the measured target coordinates were considerably accurate in this study, with absolute mean of error 4.1 mm and absolute standard deviation of error ±4.5 mm.

As an extension of Theorem 1, the mean of error upper bound of LLS-RSS is given by Lemma 2. Theorem 2. In a LLS-RSS expressed as ( {widehat{A}}_i{{widehat{boldsymbol{x}}}_i}^{mathrm{T}}={widehat{boldsymbol{b}}}_i+varDelta {widehat{boldsymbol{b}}}_i ), ΔÂ i is a matrix constructed by the minimum upper bound of localization error of corresponding anchors.

The mean of error terms was set to zero.

The estimated prevalence of overweight and obesity was 16.4% with no instrument error, corresponding to measurements taken by calibrated instruments, with the mean of error term set to zero.

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Figure 4 Mean of errors at track end as the function of initial heading error.

Figure 5 Mean of errors at track end as the function of heading change error.

Accuracy: (a) Error calculation: calculates the arithmetic mean of errors in each comparison.

Error calculation: calculates the arithmetic mean of errors in each comparison.

The performance of the proposed estimator is compared with the HCRB by means of error ellipses.

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