In this study, in order to identify the best correlation between the independent variables and the EWL, two feed-forward MLPs were used, one for non linear mapping of variables (x) into a single score u, the other one for linear mapping of EWL (y) into a score v.
One can then use these two ambiguity-fixed EWL observations, ( {overset{check{phi}}_{left l,m,nright)} ) and ( {overset{check{phi}}_{left p,q,rright)} ), and one NL observation, ϕ i, j, k), to form a GIF combination.
With its safe track record and the reported average weight loss of about 50% EWL over two to three years, the AGB is very attractive.
Table 3 shows the results of the regression analysis comparing anticipated EWL to no EWL.
In other words, the model tried to classify patients into four possible classes of EWL, considering the selected variables.
Second, in order to fix ambiguity rapidly and reliably, ambiguities are divided into three types (extra-wide-lane (EWL), wide-lane (WL) and narrow-lane (NL)) according to their wavelengths and are to be fixed sequentially by using the LAMBDA method.
All possible combinations of explanatory variables (from one to four) were computed in a multiple linear regression with EWL as the dependent variable, and a list of the values of R2, adjusted R2, p-value and the standard deviation for each linear model was extracted.
In this paper, with real GPS and BDS triple-frequency observations, we analyse the influence of the carrier-phase multipath in three typical triple-frequency combinations: extra-wide-lane (EWL) combination, ionosphere estimation with ambiguity-corrected EWL/wide-lane (WL) combinations and the geometry-free and ionosphere-free (GIF) combination for narrow-lane (NL ARR.
Seven variables (main effects) and their interactions accounted for 26.8% of EWL variability, greater than the linear model (R2 = 10%) but lower than the nonlinear model which explains 36.5% variability (Table 7).
