查询词典 approximation in excess

The new method is a combination of characteristic approximation to handle the convection part, to ensure the high stability of the method in approximating the sharp fronts and reduce the numerical diffusion, a smaller time truncation is gained at the same time, and a mixed finite elementspatial approximation to deal with the diffusion part, the sealer unknown and the adjoint vector function are approximated optimally and simultaneously.

此方法即为对方程的对流项沿流体流动的方向即特征方向进行离散，从而保证格式在流动锋线前沿逼近的高稳定性，消除了数值弥散现象，并得到了较小的时间截断误差；另一方面，对方程的扩散项采用混合元离散，可同时高精度逼近未知函数及其伴随向量函数，理论分析表明，此方法是稳定的，具有最优的L~2逼近精度。

The characteristic approximation is used to handle the convection part along the direc-tion of fluid namely characteristic direction to ensure the high stability of the method in approximating the sharp fronts and reduce the numerical diffusion; The mixed finite element spatial approximation is employed to deal with diffusion part and approximate the scalar unknown and the adjoint vector function optimally and simultaneously; In order to preserve the integral conservation of the method, we introduce the modified characteristic method.

该方法对方程的对流部分沿流体流动的方向即特征方向离散以保证格式在流动的锋线前沿逼近的高稳定性，消除数值弥散现象；对方程的扩散部分采用最低次混合有限元方法离散、同时以高精度逼近未知函数及未知函数的梯度；为保证方法的整体守恒性，在格式中引入修正项。

Furthermore, the issues of the approximation of credibility function and the convergence of the approximation method are treated.

进一步，本文考虑可信性函数的逼近方法并证明该方法的收敛性。

The zonal translation networks on the unit sphere is constructed with the best approximation of spherical harmonic polynomials and the Riesz means. The Jackson theorem of approximation by such kind of zonal translation is established.

借助于球调和多项式的最佳逼近多项式和Riesz平均构造出了单位球面S上的带形平移网络，并建立了球面带形平移网络对LS(上标 q中函数一致逼近的Jackson型定理。

Wang-type fuzzy logic system and its functional approximation ability are researched deeply, A class of Gauss-type fuzzy logic system is presented and studied What it defers from Wang-type fuzzy logic system is its using Gauss fuzzifier The analytical expression of this fuzzy logic system is derived detailed Its universal approximation theorem for nonlinear functions is proved by StoneWeiestrass theorem.

通过对两类模糊系统作为非线性辨识器的仿真研究显示出高斯型模糊逻辑系统在抗数据干扰方面比Wang型模糊系统有优势。对非线性动态系统的辨识问题，借助递归神经网络的研究成果，提出一种基于递归网络结构的自适应模糊逻辑系统和参数学习算法。

By the best approximation theory, it is first proved that the SISO (single-input single-output) linear Takagi-Sugeno fuzzy systems can approximate an arbitrary polynomial which, according to Weierstrass approximation theorem, can uniformly approximate any continuous functions on the compact domain.

借助于最优逼近理论，证明了线性SISO TS模糊系统可以逼近任意一个多项式，然后以Weierstrass逼近定理为桥梁，证明了该模糊系统可以以任意精度逼近一个任意的连续函数，从而得到了该模糊系统万能逼近性的一个新的充分条件。

Finaly, we introduce the Bernstein-Kantorovich quasi interpolant and study the approximation equivalent theorem with pointwise modulus (o≤λ≤1 ) in space L[0,1] and approximation equivalent theorem with Ditzian-Totik modulusin space L_[0,1](1 ≤p ≤ω).

最后，引入Bernstein-Kantorovich拟中插式，利用点态光滑模ω_~(2r)(0≤λ≤1)讨论了其在L_∞[0，1]空间的逼近等价定理和其在L_p[0，1](1≤p≤∞)空间关于模ω_ψ~(2r)的逼近等价定理。

In this paper we use pointwise modulus of smoothness to study approximation direct theorem and equivalent theorem for some linear operators and quasi-interpolant operators; Using pointwise modulus we discuss the strong converse inequality on K-functional; and using a modified weighted K-functional and weighted modulus of smoothness we study approximation with Jacobi weight on operator with non-zero first order moments.

本文利用点态光滑模ω_~(2r)来研究某些线性算子及逆中插式逼近正定理和等价定理；利用点态光滑模讨论其关于K-泛函的强逆不等式；同时利用一种改变的带权K-泛函和带权光滑模研究一阶矩不为零的算子的点态带Jacobi权逼近。

In approximation theory,the approximation of non-smooth function |x| is a very classical problem.

在逼近论中，非光滑函数|x|的逼近问题是一个非常经典的问题。

The study of direct and inverse theorems on the approximation of linear operators to functions in normed linear spaces is an important subject in the approximation theory. It is significant in theory and application.

线性算子对赋范线性空间中函数逼近正逆定理的研究是逼近论中重要的研究课题之一，在理论和实际应用上都具有重要的意义。

More direct, directer ; most direct, directest

径直的，笔直的

Do you know how to use a chain saw?

你知道如何使用链锯吗？