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We are interested in the approximation of a steady hyperbolic problem.
In this paper, Set Membership functions estimation methodology is employed in the approximation of a given predictive control law.
Zones of convergence and divergence of the response series are presented graphically, for a range of the non-dimensional non-linear parameter and the number of terms included in the approximation of a response harmonic.
In this paper, we are interested in the approximation of a stochastic generalized Swift-Hohenberg equation with quadratic and cubic nonlinearity by using the natural separation of time-scales near a change of stability.
end{aligned}In the approximation of a function u(x) on [a, b], in terms of continuous orthogonal polynomials (phi _i(x)), we use the expansion (u(x)=sum _{i=0}^{N}c_{i}phi _i(x)), where the coefficients (u_{i}) are computed by begin{aligned} c_{i}=frac{1}{Vert phi _iVert }int _{a}^b w(x u(x phi _i(x mathrm{d}x.
Applying approximation (3.3) and the second order of the accuracy in x in the approximation of A, we get the following values of the p function in the grid points: p n = − 1 h 2 ( ( φ n + 1 − v n + 1 0 ) − 2 ( φ n − v n 0 ) + ( φ n − 1 − v n − 1 0 ) ) + ( φ n − v n 0 ) n = 1, …, M − 1. (4.4).
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The problem of the designing bodies of minimum drag for a specified area of the base and a specified area of the ("windward") surface around which the flow occurs is considered in the approximation of an arbitrary localized interaction model.
In order to assist in the approximation of an arrival process of the aggregated minisource let the MMPP-2 process be as (1).
The analysis results are validated in numerical experiments on the approximation of a 1D Fourier kernel and of solving a 2D Poisson's equation.
This particular paradigm quantifies the ability to respond to and filter auditory information, and is one parameter that can be analysed in mice as an endophenotype; the approximation of a similar response known to be abnormal in schizophrenia (5).
This is because the approximation of (a) in (14) is negligible at high SNR.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com