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They have also studied some shape preserving and convergence properties on approximation concerning the generalized Bernstein operators (B_{n}^{mu}(f;x)).
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This approximation concerned only 6% of the follow-up.
We finally provide some useful approximations concerning the practical implementation of an α-stable adaptive equaliser.
The consequences of using some of the normal approximations concerning to the geometry, boundary conditions and type of model used, are evaluated.
We examine the approximation concern for the discrete-time stochastic GRNs with the leakage delays, distributed delays, and probabilistic measurement delays into the problem and model the robust (H_{infty}) state estimator for a class of discrete-time stochastic GRNs.
In this paper, we have studied the approximation concern for the discrete-time stochastic GRNs with the leakage delays, distributed delays, and probabilistic measurement delays into the problem and modeled the robust (H_{infty}) state estimator for a class of discrete-time stochastic GRNs.
Second, we provide various approximation results concerning the classical Korovkin theorem via Fibonacci type statistical convergence.
In paper [11], Bardaro et al. obtained some approximation results concerning the pointwise convergence and the rate of pointwise convergence for non-convolution type linear operators at a Lebesgue point.
In this section, we give quantitative estimates concerning approximation with the following theorem.
In this section, we present strong convergence theorems concerning approximation of common solution to a finite system of equilibrium problems which is also a common fixed point of a family of left Bregman strongly nonexpansive mappings in reflexive real Banach space.
The phase equilibrium compositions have been used as input data to solve a set of equations raised following four different approximations mainly concerned with the composition dependence of the interaction parameters.
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