Sentence examples for using the linear function from inspiring English sources

The black broken line illustrates fitted lines using the linear function.

In order to extract the trends, we fit the data by the least squares method using the linear function (black broken line).

Specifically, using the linear function in Eq. (8) and A[ l]=X[ l]F T, we have hat{{text{boldmath{(mathrm{A})}}}}=mathbf{X}{mathbf{F}^{T}}+{mathbf{E}}.

When using the linear function as the kernel function, with the algorithm convergence at 205 iterate steps, there are only 35 support vectors, which is the least of the four kernel functions.

Comparison of the three methods used for calculation of the total amount of emission showed that they are all acceptable, but the best method for representing data of MGM in short times (5 h) is using the linear function, whereas the use of a linear function for representing the trend of NH3 emission in longer duration (day) is not justified.

Therefore, by using the linear function reported in Fig. 6b, it is possible to calculate the average temperature of the sample by means of the following equation: Fig. 6 Absorption spectra of the sample for different values of temperature (a) and linear fit of the intensity of the DNA absorption peak (at λ = 260 nm) versus the temperature (b).

We would recommend using the less conservative approach, using the linear functions fitted to the point estimates in this literature review, in sensitivity analyses.

Thus, using the linear approximations derived from Equation 3 in Bauch et al. (2009) [ 12] for both age groups would provide a conservative estimate of herd effect, while using the linear functions fitted to data from this structured literature review would provide a less conservative estimate of herd effect.

While the preference degree can be expressed by two types of functions: linear and Gaussian, this research uses the linear function, which requires two parameters to determine each preference: an indifference threshold (q) and the preference threshold (p).

The detailed algorithm for estimating the missing extremum point is shown in Algorithm 4. As shown in the algorithm, we use the linear function to estimate approximate extremum point if the condition in Theorem 4 is not satisfied, and more complicated situation will be considered in our future works.

In the least-squares fitting, we use the linear function written as: where β j is the slope for j-th sliding window and ε ij is a noise term.