散乱数据带自然边界条件二元样条光顺及数值微分

考虑一种新的散乱数据带自然边界二元样条光顺问题.根据样条变分理论和Hilbert空间样条函数方法,构造出了显式的二元带自然边界光顺样条解,其表达式简单且系数可以由系数矩阵对称正定的线性方程组确定.证明了解的存在和唯一性,讨论了收敛性和误差估计.并由此得到一种新的基于散乱数据上的正则化二元数值微分的方法.最后,给出了一些数值例子对方法进行了验证.     

含退化边三角剖分下二元三次C~1样条空间的维数

<正>1二元三次一阶光滑样条函数二元样条函数空间在数值逼近、曲面拟合、有限元方法(FEM)、散乱数据插值、多元数值积分、微分和积分方程数值解、计算机辅助几何设计(CAGD)、计算机图形学、信号过程和数学模型等领域有着广泛的应用.而空间S_3~1(Δ)除了二元三次样条函数具有的计     

一种广义周期Besov类的多元周期多项式样条逼近

本文证明多元多项式周期样条空间是某些多元周期光滑函数类的关于Kolmogorov n-宽度的弱渐近极子空间．给出了广义周期Besov类的一种推广，得到了空间元素的一种表示定理，不仅给出了一种多元周期多项式样条算子．而且证明了所得的结果．     

散乱数据的多项式自然样条光顺与广义插值

由于理论与实践的重要性,在多元插值方面有相当多的工作,如[1]-[11]。目前以箱样条(box splines),光滑余因子与B网方法以及薄板样条与径函数(radial basis function)方法比较活跃。前者具有良好的性质和丰富的结构,很快成为一个活跃的研究方向,最近更在小波(wavelet)变换理论研究上发挥了作用。但是,它一般只处理规则分划的问题,不能做多元散乱数据的插值。     

样条函数空间的维数级数和基函数

本文考虑多元样条函数维数级数和基函数的计算．文［２］，［３］中，讨论了通过ｄ－１维面上的光滑连接条件，用Ｇｒ?ｂｎｅｒ基方法计算多元样条函数的维数级数和基函数．事实上，样条函数的结构可由ｄ－２维面上协调方程决定．本文通过构造合冲序列及Ｇｒ?ｂｎｅｒ基的性质，推导协调矩阵与维数级数的关系，给出了由协调矩阵的核空间计算样条函数基函数的方法．     

广义有理样条的几种构造方法

本文利用Ｔｈｉｅｌｅ倒差分方法、Ｐａｄｅ逼近方法、广义Ｑ．Ｄ．算法及ε－算法等构造了几种广义有理样条函数．此外，通过直接法构造了（ｋ－１，ｋ）－型广义有理样条，给出了它的行列式表示和余项表示并证明了广义有理样条算子的存在性、唯一性、齐次性及连续性．     

样条曲线光顺的数学模型分析

采用函数三次样条光顺曲线,证明在样条曲线局部转角小,总转角不超过120°情况下,曲线的光顺指示函数y″(1+y′2)3/2可以简化为二阶导数曲线y″(x).由于y″(x)对x是分段折线函数,对y是线性泛函,因而定出不光顺之处及用叠加原理计算调整公式均变得很简单.此样条函数曲线光顺能够采用电脑自动化进行.     

n维散乱数据带自然边界条件多元多项式样条插值

考虑n维散乱数据Hermit-Birkhoff型插值问题,在使给定的目标泛极小的条件下,构造了一种带自然边界条件的多元多项式样条函数插值方法.重点研究了插值问题解的特征,存在唯一性和构造方法,并讨论了收敛性及误差,最后给出了一些数值算例对方法进行验证.     

几种基于散乱数据拟合的局部插值方法

本文首先针对散乱数据拟合的Shepard方法,结合截断多项式、B样条基函数和指数函数来构造其权函数,使新的权函数具有更高的光滑度和更好的衰减性,并且其光滑性和衰减性可以根据实际需要自由调节,从而提高了曲面的拟合质量．同时还给出一种类似的局部插值方法．另外,本文还基于多重二次插值,结合多元样条的思想,给出了两个局部插值算法．该算法较好地继承了多重二次插值曲面的性质,从而保证了拟合曲面具有好地光顺性和拟合精度．曲面整体也具有较高的光滑性．     

广义变系数模型的Bayesian B样条估计

提出了广义变系数模型函数系数的一种新的估计方法．我们用B样条函数逼近函数系数,不具体选择节点的个数,而是节点个数取均匀的无信息先验,样条函数系数取正态先验,用Bayesian模型平均的方法估计各个函数系数．这种估计方法一个主要特点是允许各个函数系数所需节点个数的后验分布不同,因此允许不同函数系数使用不同的光滑参数．另外,本文还给出了Bayesian B样条估计的计算方法,并通过模拟例子,说明广义变系数模型的函数系数可以由Bayesian B样条估计方法得到很好的估计．     

Bivariate Polynomial Natural Spline Interpolation Algorithms with Local Basis for Scattered Data

Because of its importance in both theory and applications, multivariate splines have attracted special attention in many fields. Based on the theory of spline functions in Hilbert spaces, bivariate polynomial natural splines for interpolating, smoothing or generalized interpolating of scattered data over an arbitrary domain are constructed with one-sided functions. However, this method is not well suited for large scale numerical applications. In this paper, a new locally supported basis for the bivariate polynomial natural spline space is constructed. Some properties of this basis are also discussed. Methods to order scattered data are shown and algorithms for bivariate polynomial natural spline interpolating are constructed. The interpolating coefficient matrix is sparse, and thus, the algorithms can be easily implemented in a computer.     

加密网格点二元局部基插值样条函数

1.简介 由于在理论以及应用两方面的重要性,多元样条引起了许多人的注意([6],[7]),紧支撑光滑分片多项式函数对于曲面的逼近是一个十分有效的工具。由于它们的局部支撑性,它们很容易求值;由于它们的光滑性,它们能被应用到要满足一定光滑条件的情况下;由于它们是紧支撑的,它们的线性包有很大的逼近灵活性,而且用它们构造逼近方法来解决的系统是     

Sur le choix du paramètre d'ajustement dans le lissage par fonctions spline

Summary We consider the problem of approximating an unknown functionf, known with error atn equally spaced points of the real interval [a, b].To solve this problem, we use the natural polynomial smoothing splines. We show that the eigenvalues associated to these splines converge to the eigenvalues of a differential operator and we use this fact to obtain an algorithm, based on the Generalized Cross Validation method, to calculate the smoothing parameter.With this algorithm, we divide byn the time used by classical methods.

     

Selecting the Number of Knots for Penalized Splines

Penalized splines, or P-splines, are regression splines fit by least-squares with a roughness penalty.P-splines have much in common with smoothing splines, but the type of penalty used with a P-spline is somewhat more general than for a smoothing spline. Also, the number and location of the knots of a P-spline is not fixed as with a smoothing spline. Generally, the knots of a P-spline are at fixed quantiles of the independent variable and the only tuning parameters to choose are the number of knots and the penalty parameter. In this article, the effects of the number of knots on the performance of P-splines are studied. Two algorithms are proposed for the automatic selection of the number of knots. The myopic algorithm stops when no improvement in the generalized cross-validation statistic (GCV) is noticed with the last increase in the number of knots. The full search examines all candidates in a fixed sequence of possible numbers of knots and chooses the candidate that minimizes GCV.The myopic algorithm works well in many cases but can stop prematurely. The full-search algorithm worked well in all examples examined. A Demmler–Reinsch type diagonalization for computing univariate and additive P-splines is described. The Demmler–Reinsch basis is not effective for smoothing splines because smoothing splines have too many knots. For P-splines, however, the Demmler–Reinsch basis is very useful for super-fast generalized cross-validation.     

The hat matrix for smoothing splines

The matrix which transforms the data vector to the vector of fitted values for smoothing splines is termed the hat matrix. This matrix is shown to have many of the same properties, and is seen to play the same role in the variances and covariances of the residuals, as its regression analysis counterpart. This fact is utilized to propose several possible diagnostic measures for use with smoothing splines. The extension of these results to include multivariate Laplacian smoothing spline is also indicated.     

Analysis of Multi-Unit Variance Components Models with State Space Profiles

We apply the Kalman Filter to the analysis of multi-unit variance components models where each unit's response profile follows a state space model. We use mixed model results to obtain estimates of unit-specific random effects, state disturbance terms and residual noise terms. We use the signal extraction approach to smooth individual profiles. We show how to utilize the Kalman Filter to efficiently compute the restricted loglikelihood of the model. For the important special case where each unit's response profile follows a continuous structural time series model with known transition matrix we derive an EM algorithm for the restricted maximum likelihood (REML) estimation of the variance components. We present details for the case where individual profiles are modeled as local polynomial trends or polynomial smoothing splines.     

On spline functions and statistical regularization of ill-posed problems

Interpolating natural splines are used for the algebraization and smoothing regularization of linear Fredholm integral equations of the first kind. A simplified version of statistical regularization is presented and, in turn, applied to data graduation by smoothing natural splines.     

Smoothing splines using compactly supported, positive definite, radial basis functions

In this paper, we develop a fast algorithm for a smoothing spline estimator in multivariate regression. To accomplish this, we employ general concepts associated with roughness penalty methods in conjunction with the theory of radial basis functions and reproducing kernel Hilbert spaces. It is shown that through the use of compactly supported radial basis functions it becomes possible to recover the band structured matrix feature of univariate spline smoothing and thereby obtain a fast computational algorithm. Given n data points in R ², the new algorithm has complexity O(n ²) compared to O(n ³), the order for the thin plate multivariate smoothing splines.     

函数系数线性自回归模型的样条估计

基于多项式样条全局光滑方法,建立函数系数线性自回归模型中系数函数的样条估计.在适当条件下,证明了系数函数多项式样条估计的相合性,并给出了它们的收敛速度.模拟例子验证了理论结果的正确性.     

Apporoximation of contact geometry in the dynamical simulation of wheel-rail

Models for the geometrical contact of wheel and rail are a basic component of multibody system (MBS) models for wheel-rail systems. Approximations are used to get sufficiently differentiable contact conditions that can be evaluated efficiently. We discuss an approximation that is essentially based on polynomial 2D-tensorproduct splines and minimizes a functional that combines a weighted least squares approximation of a rigid contact model with a smoothing term. An efficient algorithm to compute the tensorproduct spline is developed, the parallelization on a cluster of workstations is discussed. We report on results of both the sequential and the parallel algorithm and give simulation results for a rigid wheelset on a straight track.