Sentence examples for basis are compared from inspiring English sources

Least square error fitting techniques employing a classic monomial and a Forsythe orthogonal polynomial basis are compared by starting from numerically estimated measurements with noise.

In Table 1, the error norms (L_{2}) and (L_{infty}) of numerical solutions with IQ basis are compared for (c=10^{-15} c=10^{-15}and=0.01) at times (t=1).

A paired t-test was used to compare the mean scores for the anatomic visualization of the brachial plexus on a per region basis was compared for conventional MRI alone and the combination of conventional MRI and DW-MRN.

The results obtained with the contracted basis sets are compared to the values obtained with our extended basis sets and to the standard 6-311G baset set from literature.

The numerical response solutions using Gaussian radial basis functions, are compared to those obtained by using power-series and third order Hermite polynomials.

The optimized structural parameters (bond lengths and bond angles) calculated by DFT/B3LYP with 6-31 + G d,p) basis set are compared with experimentally available X-ray data for benzylidene [14] and coumarin [15].

The predictions of polymer polymer miscibility on the basis of spinodal lines are compared with the corresponding predictions on the basis of χ interaction parameters.

The results of the lifetime estimation generated by means of the new concept on the basis of microcrack growth are compared and verified with those experiences obtained from multiaxial fatigue testing.

In this study, predictions from a basis model (without baffles) are compared with those from four different configurations including: (i) 5-cm baffles at 5-m spacing, (ii) 7.5-cm baffles at 5-m spacing, (iii) 10-cm baffles with 5-m spacing, (iv) 10-cm baffles at 2.5-m spacing, and (v) 10-cm baffles at 1-m spacing.

On the basis, various similar approaches are compared in Table 1.

Linear support vector regression (SVR) and nonlinear radial basis function (RBF) SVR are compared with multiple linear regression (MLR).