In contrast, the main goals of the D study are to deduce or explain the measurement results according to the specific decision needs, reconstruct a variety of generalized regions, estimate the size of the variance components at the level of the sample mean, and then estimate the various measurement errors and measurement accuracy indices (relative error and GC or

absolute error and DI) to provide valuable information that can be used to improve measures (Yang & Zhang, 2003).

Maximum

absolute error for Example 1 for different values of e with N = 23 (initially) [epsilon] Max.

It was observed that the models developed through DM techniques presented the smallest

absolute errors relative to the ARIMA model.

The

absolute error of porosity was still large, but due to the reduction of the heat transfer time, it was reduced from about 11.4 to 8.2%.

Parameter SIDFT RVCI Mean of absolute values of errors 0.0214 0.0084 Maximum

absolute error 0.0447 0.0191 Rms error 0.0258 0.0104 Probability of

absolute error less than 0.001 (bin) 24.8% 44.6% Probability of

absolute error less than 0.005 (bin) 46.2% 78.0% Probability of

absolute error less than 0.01 (bin) 57.3% 94.5% Parameter SIDFT RVCI + AS + AS Mean of absolute values of errors 0.0005 0.0005 Maximum

absolute error 0.0355 0.0326 Rms error 0.0017 0.0015 Probability of

absolute error less than 0.001 (bin) 90.74% 90.57% Probability of

absolute error less than 0.005 (bin) 98.13% 98.17% Probability of

absolute error less than 0.01 (bin) 99.32% 99.32%

Table 2 lists the mean and standard deviation of the

absolute error.

Figure 8 also shows that the relative magnitude of the cost function J/[J.sub.1] and the mean

absolute error (MAE) of observation points of the adjoint model using the characteristic finite difference (CFD) scheme decline more quickly than the central difference scheme (CDS).

60 remained data were used as the test data into the best prediction model to calculate the average

absolute error percentage.

Figure 1 presents the

absolute error of ADM with Bernstein polynomial in (a) and ADM with modified Bernstein polynomial in (b) at m=v=6 and k=2.

Figures 2, 3, 4, 5, 6, 7, and 8 illustrate the performance comparison among build classification models using accuracy, true positive rate, precision, F-measure, kappa statistic, mean

absolute error, and root mean squared error rates, respectively.

The

absolute error between measured flow [V.sub.U], which was measured by the ultrasonic meter, and the calculated flow [V.sub.C], which was calculated using the corrected valve command, was taken for the first set of errors [Error.sub.1].

Now, we will use the bound in (3.10) to estimate the

absolute error |[[lambda]*.sub.l] - [[lambda].sub.l,N]| when [[lambda]*.sub.l] is the exact eigenvalue of the problem (1.5)-(1.6) and [[lambda].sub.l,N] is the zero of the function [[DELTA].sub.l,N]([lambda]).