Sentence examples for a norm method to from inspiring English sources
When the correlation coefficient ρ ns of T_V a and T_V n belongs to (1−Δ ρ 1−Δ ρ 2,1−Δ ρ 1], the linear relationship between T_V a and T_V n is not so obvious. Therefore, the reasonableness of the recommended trust value needs a further discussion. We introduce a norm method to discuss reasonableness of the recommendation trust.
This study combines the ε-constraint method and weighted infinite norm method to solve the primal multi-objective optimization problem.
The multi-stiffness topology optimization is performed using a norm method with weighting coefficients.
We then subtracted the two matrices and computed the Frobenius norm (Methods) to quantify the distance between the two data sets.
We propose a modified P-norm correction method to overcome the limitation of conventional P-norm methods by employing the lower bound P-norm stress curve.
Due to the non-smooth nature of the 1-norm, a simple method to solve these problems is the subgradient approach [37], which converges only as O ( 1 k ), where k is the iteration counter.
Using a fixed point theorem of cone expansion and compression of norm type and a new method to deal with the impulsive term, we prove that the second-order singular impulsive Neumann boundary value problem has denumerably many positive solutions.
Butler, Reeds, and Dawson (BRD) proposed a method to solve norm smoothing with a non-negative constraint of solution (Butler et al. 1981).
The pioneer work in this framework was put forward by Saminger-Platz et al. in [1] which provides a method to extend a t-norm T from a complete sublattice M to a bounded lattice L. Later, we have developed in [2] an extension method to extend t-norms, t-conorms, and fuzzy negations that generalizes the method proposed in [1] considering a modified notion of sublattice.
Finally, we suggest a purely analytic method to further investigate these norms which up to now has been lacking.
The weighted infinite norm method is a reference-goal method that can conveniently determine a trade-off solution if the lower and upper bounds of each objective value are known in advance.
