Sentence examples for generalized weights from inspiring English sources

The first part of Sect. 2 provides some short background about Markov-type inequalities in (L^2) spaces with classical weights and the second one deals with a Markov-type inequality corresponding to each weighted Sobolev norm with respect to these classical weights and to some generalized weights (see Theorem 2.1).

It is shown that the CRPF and APF have a similar form when the cost functions in CRPF are considered as the generalized weights.

A generalized weight-based particle simulation scheme suitable for simulating magnetized plasmas, where the zeroth-order inhomogeneity is important, is presented.

A generalized weight update law is proposed for KSOM based inverse kinematic control, to resolve the redundancy during the learning phase.

Each caregiver was assigned an estimation weight based on the generalized weight sharing method enabling estimation of representative results at national level [28].

In the present section, we introduce the so-called generalized weight numbers, as was done for the case of operator (1) with Robin boundary conditions (see [11, 12]) and study the properties of the generalized spectral data.

Theorem 2 The coefficients M n and the generalized weight numbers α n determine each other uniquely by the formula ∑ j = 0 ν α n + ν − j M n + m n − j − 1 = − δ ν, 0, n ∈ S, ν = 0, m n − 1 ¯.

Thus, { S n ( x ) } n ≥ 1, { ψ n ( x ) } n ≥ 1 are complete systems of eigen- and the associated functions of the boundary value problem L. Together with the eigenvalues λ n we consider generalized weight numbers α n, n ≥ 1, determined in the following way: α k + ν = ∫ 0 T S k + ν ( x ) S k + m k − 1 ( x ) d x, k ∈ S, ν = 0, m k − 1 ¯.

22 We assigned a generalized weighting matrix, which was standardized to sum to one.

The improved performance of our proposed generalized bootstrap weights over design bootstrap weights is illustrated by means of a simulation study.

The generalized weighted quasiarithmetic mean of function with respect to weight function is given by (21).