球面混合插值的逼近性质

球面径向基函数(SBF)和多项式样条函数均为处理球面散乱数据的有效工具. 本文考虑由球面径向基函数与球面多项式函数组成的混合插值模型, 并利用最小二乘法求解该模型. 对于该插值模型, 首先, 给出带Bessel势的Sobolev空间中的Bernstein不等式, 然后利用该不等式建立逼近正定理,并进一步给出该插值工具的误差估计. 最后, 研究该插值方式(即利用最小二乘法求解混合插值模型)的稳定性.     

关于径向基函数插值的收敛性

本文在n维空间给出了径向基函数插值及逼近的收敛性质,并给出了收敛阶。     

关于径向基函数插值的收敛性

本文在 n 维空间给出了径向基函数插值及逼近的收敛性质,并给出了收敛阶.     

求解Bratu型方程的径向基函数逼近法

基于径向基函数可以逼近几乎所有函数的强大逼近功能,借鉴弹塑性静力学的处理方法,提出位移、速度、加速度联合插值的径向基函数表达式,结合MATLAB数值软件进行计算机编程,成功求解了Bratu型强非线性方程,并给出相应的相对误差.通过分析几种典型的算例,并将计算结果与一些现有的数值分析法得到的数值解进行对比,表明了该方法的可行性和精确性,为求解强非线性Bratu型方程提供了一种新思路.     

球面光滑核漂移的Lp逼近

将光滑的球面基函数φ嵌入到由一个不充分光滑的球面基函数ψ生成的本性空间Nψ中,并在Lp度量下研究由φ的变换生成的函数在空间Nψ中的逼近性质,得到了该Lp逼近的误差估计.     

结构动力响应中急动度的计算

急动度(jerk)在工程实践中具有重要的意义.将径向基函数逼近与配点法相结合,发展了一种能够有效求解动力响应的数值算法.该方法使用径向基函数插值来逼近真实的运动规律,能够用于急动度和急动度(三阶)方程的计算,弥补了传统的数值方法无法计算急动度的不足.并针对微分方程的特点,提出了改进的多变量联合插值函数,同时添加与微分方程同阶的初值条件,可显著减小数值震荡.算例表明,该方法具有计算过程简单、精度高的特点,同时对急动度方程也有很好的适用性.     

用径向基函数插值解自共轭椭圆型方程

本文讨论用MQ作为插值的径向基函数,对自共轭椭圆型方程进行插值,证明了插值系数的唯一性,并用投影法证明了用径向基函数解自共轭椭圆型方程的收敛性.     

球面光滑核漂移的L~p逼近

将光滑的球面基函数φ嵌入到由一个不充分光滑的球面基函数Ψ生成的本性空间N_Ψ中,并在L~p度量下研究由φ的变换生成的函数在空间N_Ψ中的逼近性质,得到了该L~p逼近的误差估计.     

径向基网络的非线性插值与3D点云曲面重构技术

研究了径向基网络插值算法与3D曲面重构方法,分别从研究价值、插值理论、仿真等等方面做了详细分析,研究结果表明RBF-Network在逼近一维复杂的非线性函数时具有收敛速度快、精度高、泛化能力更强等优点.但在3D重构方面,基于RBF的单位分解法重构效果更好.     

神经网络中的逼近问题

本文主要讨论径向基神经网络对函数，连续泛函及连续算子的逼近．     

球面卷积算子逼近

研究d维欧氏空间R~d中单位球面上卷积算子的逼近问题.利用球面乘子理论以及K-泛函与光滑模等价关系,建立一类球面卷积算子逼近的正、逆定理.特别地,给出了逼近的强型逆向不等式,从而揭示了该类球面卷积算子的本质逼近阶.此外,作为应用,给出了球面Jackson-Matsuoka卷积算子与Abel-Poisson卷积算子逼近上、下界的相同阶估计.     

球型平移网络逼近周期函数

研究了球型平移网络对周期函数的逼近问题．文章首先将基函数eimx分别表示成为两种球型平移网络．进一步,将有关多重Fourier级数的Bochner-Riesz平均表示成为球型平移网络的形式．在此基础上构造出了两类球型平移网络序列,并借助于有关Bochner-Riesz平均对Lp空间中函数的逼近结果给出了这两类球型平移网络序列在Lp空间中的逼近阶．     

球面带形平移网络逼近的Jackson定理

研究了球面带型平移网络逼近阶用球面调和多项式的最佳逼近及光滑模的刻画问题．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Sq上的带形平移网络,并建立了球面带形平移网络对Lp(Sq)中函数一致逼近的Jackson型定理．所得结果表明球面带形平移网络可以达到球调和多项式的逼近阶．     

A Petrov–Galerkin RBF method for diffusion equation on the unit sphere

This paper concerns a numerical solution for the diffusion equation on the unit sphere. The given method is based on the spherical basis function approximation and the Petrov–Galerkin test discretization. The method is meshless because spherical triangulation is not required neither for approximation nor for numerical integration. This feature is achieved through the spherical basis function approximation and the use of local weak forms instead of a global variational formulation. The local Petrov–Galerkin formulation allows to compute the integrals on small independent spherical caps without any dependence on a connected background mesh. Experimental results show the accuracy and the efficiency of the new method.     

Error estimates for scattered data interpolation on spheres

We study Sobolev type estimates for the approximation order resulting from using strictly positive definite kernels to do interpolation on the -sphere. The interpolation knots are scattered. Our approach partly follows the general theory of Golomb and Weinberger and related estimates. These error estimates are then based on series expansions of smooth functions in terms of spherical harmonics. The Markov inequality for spherical harmonics is essential to our analysis and is used in order to find lower bounds for certain sampling operators on spaces of spherical harmonics.

     

Scattered data quasi‐interpolation on spheres

This paper studies the construction and approximation of quasi‐interpolation for spherical scattered data. First of all, a kind of quasi‐interpolation operator with Gaussian kernel is constructed to approximate the spherical function, and two Jackson type theorems are established. Second, the classical Shepard operator is extended from Euclidean space to the unit sphere, and the error of approximation by the spherical Shepard operator is estimated. Finally, the compact supported kernel is used to construct quasi‐interpolation operator for fitting spherical scattered data, where the spherical modulus of continuity and separation distance of scattered sampling points are employed as the measurements of approximation error. Copyright © 2014 John Wiley & Sons, Ltd.     

On the Degree of Approximation by Spherical Translations

The degree of approximation of spherical functions by the translations formed by a function defined on the unit sphere is dealt with. A kind of Jackson inequality is established under the condition that none of the L^2（S^q） norms of the orthogonal projection operators of the translated function are zeros. In the present paper we show that the spherical translations share the same degree of approximation as that of spherical harmonics.     

The estimate for approximation error of spherical neural networks

Compared with planar hyperplane, fitting data on the sphere has been an important and an active issue in geoscience, metrology, brain imaging, and so on. In this paper, with the help of the Jackson‐type theorem of polynomial approximation on the sphere, we construct spherical feed‐forward neural networks to approximate the continuous function defined on the sphere. As a metric, the modulus of smoothness of spherical function is used to measure the error of the approximation, and a Jackson‐type theorem on the approximation is established. Copyright © 2011 John Wiley & Sons, Ltd.     

A boundary parametric approximation to the linearized scalar potential magnetostatic field problem

We consider the linearized scalar potential formulation of the magnetostatic field problem in this paper. Our approach involves a reformulation of the continuous problem as a parametric boundary problem. By the introduction of a spherical interface and the use of spherical harmonics, the infinite boundary conditions can also be satisfied in the parametric framework. That is the field in the exterior of a sphere is expanded in a ‘harmonic series’ of eigenfunctions for the exterior harmonic problem. The approach is essentially a finite element method coupled with a spectral method via a boundary parametric procedure. The reformulated problem is discretized by finite element techniques which leads to a discrete parametric problem which can be solved by well conditioned iteration involving only the solution of decoupled Neumann type elliptic finite element systems and L² projection onto subspaces of spherical harmonics. Error and stability estimates given show exponential convergence in the degree of the spherical harmonics and optimal order convergence with respect to the finite element approximation for the resulting fields in L².     

On approximation by spherical reproducing kernel Hilbert spaces

The spherical approximation between two nested reproducing kernels Hilbert spaces generated from different smooth kernels is investigated. It is shown that the functions of a space can be approximated by that of the subspace with better smoothness. Furthermore, the upper bound of approximation error is given.