Numerical integration on multivariate scattered data by Lobachevsky splines

In this paper, we investigate the numerical integration problem of a real valued function generally known only on multivariate scattered points using Lobachevsky splines, a pioneering version of cardinal B-splines. Starting from their interpolation properties, we focus on the construction of new integration formulas, which are quite flexible requiring no special distribution of nodes. Numerical results using Lobachevsky splines turn out to be interesting and promising for both accuracy and simplicity in computation. Finally, a comparison with integration by radial basis functions confirms the validity of the proposed approach.     

Differential constraints for bounded recursive identification with multivariate splines

The ability to perform online model identification for nonlinear systems with unknown dynamics is essential to any adaptive model-based control system. In this paper, a new differential equality constrained recursive least squares estimator for multivariate simplex splines is presented that is able to perform online model identification and bounded model extrapolation in the framework of a model-based control system. A new type of linear constraints, the differential constraints, are used as differential boundary conditions within the recursive estimator which limit polynomial divergence when extrapolating data. The differential constraints are derived with a new, one-step matrix form of the de Casteljau algorithm, which reduces their formulation into a single matrix multiplication. The recursive estimator is demonstrated on a bivariate dataset, where it is shown to provide a speedup of two orders of magnitude over an ordinary least squares batch method. Additionally, it is demonstrated that inclusion of differential constraints in the least squares optimization scheme can prevent polynomial divergence close to edges of the model domain where local data coverage may be insufficient, a situation often encountered with global recursive data approximation.     

A note on approximation of discrete data by G arc splines

Meek and Walton discussed the approximation of discrete data by G¹ arc splines in 1992. For a biarc from the start point A to the end point B, they assumed that the sum of two counterclockwise angles of the two arcs was equal to the sum of the counterclockwise angle from the start tangent vector to the vector B–A and that from the vector B–A to the terminal tangent vector. In this note, we show that the relation can be relaxed by the addition or subtraction of 2π.     

A hybrid radial basis function and data envelopment analysis neural network for classification

We propose a hybrid radial basis function network-data envelopment analysis (RBFN-DEA) neural network for classification problems. The procedure uses the radial basis function to map low dimensional input data from input space ℜ to a high dimensional ℜ⁺ feature space where DEA can be used to learn the classification function. Using simulated datasets for a non-linearly separable binary classification problem, we illustrate how the RBFN-DEA neural network can be used to solve it. We also show how asymmetric misclassification costs can be incorporated in the hybrid RBFN-DEA model. Our preliminary experiments comparing the RBFN-DEA with feed forward and probabilistic neural networks show that the RBFN-DEA fares very well.     

二元非张量积型连分式插值

首先,基于新的二元非张量积型逆差商递推算法,分别建立奇数与偶数个插值节点上的二元连分式插值格式,并得到被插函数的两类恒等式。接着,利用连分式三项递推关系式,分别确定渐近式的分子和分母的次数,即特征定理,并给出推导分子、分母的递推算法。同时,研究表明所提连分式的分子、分母次数分别小于相应的二元Thiele型插值连分式分子、分母次数,这主要是因为所提连分式插值减少了对冗余的插值节点的采用。然后,从计算复杂性的角度出发,所提二元有理函数插值的计算量小于相同插值节点上的径向基函数插值的计算量。最后,数值算例表明所提二元连分式插值方法有效且可行,同时也揭示了即使插值节点集合不变,所提插值连分式的表达式也会随着插值节点顺序的改变而改变。     

Surface reconstruction using bivariate simplex splines on Delaunay configurations

Recently, a new bivariate simplex spline scheme based on Delaunay configuration has been introduced into the geometric computing community, and it defines a complete spline space that retains many attractive theoretic and computational properties. In this paper, we develop a novel shape modeling framework to reconstruct a closed surface of arbitrary topology based on this new spline scheme. Our framework takes a triangulated set of points, and by solving a linear least-square problem and iteratively refining parameter domains with newly added knots, we can finally obtain a continuous spline surface satisfying the requirement of a user-specified error tolerance. Unlike existing surface reconstruction methods based on triangular B-splines (or DMS splines), in which auxiliary knots must be explicitly added in advance to form a knot sequence for construction of each basis function, our new algorithm completely avoids this less-intuitive and labor-intensive knot generating procedure. We demonstrate the efficacy and effectiveness of our algorithm on real-world, scattered datasets for shape representation and computing.     

Discrete approximation by variational vector splines for noisy data

We deal with the problem of constructing and/or approximating some curves and surfaces from a noisy data set. The main objective here is to adapt the theory of the discrete smoothing variational splines [A. Kouibia, M. Pasadas, Approximation by discrete variational splines, J. Comput. Appl. Math. 116 (2000) 145–156] to the noisy data case. The problem is formulated and a result of almost sure convergence is established.     

散乱数据拟合的自适应分片逆尺度空间算法

散乱数据拟合(逼近)是在信号处理、计算机图形学等领域中被广泛研究的问题, 近些年,利用优化方法获得散乱数据的稀疏表示逼近解也成为了优化和曲面重构交叉领域的热 点。基于由B 样条生成的PSI 空间中的散乱点曲面拟合问题和分片稀疏的联系,将分片稀疏性 引入到Bregman 逆尺度空间算法(ISS)中,提出一种自适应的分片逆尺度空间(aP_ISS)算法,处 理散乱数据的曲面拟合问题。通过对逆尺度空间系统分片符号一致性分析,得到了自适应分片 逆尺度空间系统的性能保证定理和避免了aP_ISS 参数的选取。应用到散乱点曲面重构问题上 的数值实验结果表明,该算法不仅可以有效拟合曲面,还能够较好保护分片稀疏性。     

Derivative generation from multivariate scattered data by functional minimization

Many multivariate interpolation schemes require as data values of derivatives that are not available in a practical application, and that therefore have to be generated suitably. A specific approach to this problem is described that is modeled after univariate spline interpolation. Derivative values are defined by the requirement that a certain functional be minimized over a suitable space subject to interpolation of given positional data. In principle, the technique can be applied in arbitrarily many variables. The theory is described in general, and a particular application is given in two variables. A major tool in the implementation of the technique is the Bézier-Bernstein form of a multivariate polynomial. The technique yields visually pleasing surfaces and is therefore suitable for design applications. It is less suitable for the approximation of derivatives of a given function.     

工程实验数据的非线性拟合方法

在EXCEL软件中,拟合非线性实验数据有4种方法。其中规划求解方法与随机抽样方法的最大优点是不要变换方程,且适合任何实验数据非线性拟合。规划求解方法最为简便,但精度相对要差,结果有时难以把握。后者加入了VBA混合编程,达到筛选与优化目的,提高了拟合精度。另2种方法为函数公式方法和回归分析方法,前者根据最小二乘法原理仅仅在电子表格中输入函数公式即可得到精度极高的拟合结果。后者利用了EXCEL自带的数学分析工具完成拟合,精度最高。但它们在应用之前,需要对实验数据进行多元多项式方程变换,对难于变换的非线性实验数据不适合。处理工程实验数据,掌握这4种非线性实验数据的拟合方法很有必要。     

离散数据拟合模型的研究与实现

最小二乘支持向量机引入到离散数据拟合中,代替传统的最小二乘法解决离散数据拟合问题。推导了用于函数估计的最小二乘支持向量机算法,构建了基于最小二乘支持向量机的离散数据拟合模型,并对电机数据拟合进行了研究。结果表明,最小二乘支持向量机拟合离散数据比最小二乘法精度更高、拟合效果更好。     

A smooth approximation for the solution of special non-linear third-order boundary-value problems based on non-polynomial splines

A second-order method based on quartic non-polynomial spline consisting of a polynomial part of degree two and a trigonometric part is developed to find continuous approximation of a two point boundary-value problem involving a third-order differential equation. It is shown that the angular frequency k of the trigonometric part can be used to raise the order of accuracy of the new scheme. Convergence of the method is shown along with numerical examples each for linear and non-linear cases to demonstrate the practical usefulness of the new method. Comparison with existing methods of the same order is given to exhibit superiority of the method.     

A coordinate system associated with points scattered on a surface

Coordinate systems associated to a finite set of sample points have been extensively studied, especially in the context of interpolation of multivariate scattered data. Notably, Sibson proposed the so-called natural neighbor coordinates that are defined from the Voronoi diagram of the sample points. A drawback of those coordinate systems is that their definition domain is restricted to the convex hull of the sample points. This makes them difficult to use when the sample points belong to a surface. To overcome this diffculty, we propose a new system of coordinates. Given a closed surface S, i.e. a (d−1)-manifold of the coordinate system is defined everywhere on the surface, is continuous, and is local even if the sampling density is finite. Moreover, it is inherently (d−1)-dimensional while the previous systems are d-dimensional. No assumption is made about the ordering, the connectivity or topology of the sample points nor of the surface. We illustrate our results with an application to interpolation over a surface.     

Optimization methods for scattered data approximation with subdivision surfaces

We present a method for scattered data approximation with subdivision surfaces which actually uses the true representation of the limit surface as a linear combination of smooth basis functions associated with the control vertices. A robust and fast algorithm for exact closest point search on Loop surfaces which combines Newton iteration and non-linear minimization is used for parameterizing the samples. Based on this we perform unconditionally convergent parameter correction to optimize the approximation with respect to the L² metric, and thus we make a well-established scattered data fitting technique which has been available before only for B-spline surfaces, applicable to subdivision surfaces. We also adapt the recently discovered local second order squared distance function approximant to the parameter correction setup. Further we exploit the fact that the control mesh of a subdivision surface can have arbitrary connectivity to reduce the L^∞ error up to a certain user-defined tolerance by adaptively restructuring the control mesh. Combining the presented algorithms we describe a complete procedure which is able to produce high-quality approximations of complex, detailed models.     

Fair, G- and C-continuous circle splines for the interpolation of sparse data points

This paper presents a carefully chosen curve blending scheme between circles, which is based on angles, rather than point positions. The result is a class of circle splines that robustly produce fair-looking G²-continuous curves without any cusps or kinks, even through rather challenging, sparse sets of interpolation points. With a simple reparameterization the curves can also be made C²-continuous. The same method is usable in the plane, on the sphere, and in 3D space.     

多维浮点数据的曲线拟合压缩存储方法

以三维位置信息为代表的多维浮点数据是当前高性能计算机系统处理的主要数据对象,对其进行压缩存储是解决海量多维数据量快速增长的需求与存储空间、存储带宽不足的矛盾的手段之一。现有数据压缩算法对浮点多维数据难以取得理想的压缩效果。将多维浮点数据投影到二维坐标上,使用多项式曲线对其进行拟合,将坐标值的存储转换为多项式系数的存储,实现了多维数据的曲线拟合压缩。算法设计中引入了排序作为数据预处理手段,从而得到了较小的压缩误差。理论分析和实验结果表明,在与现有有损压缩误差相当的条件下,得到了优于现有算法的压缩率。     

Convexity preserving scattered data interpolation using Powell–Sabin elements

The problem of strict convexity preserving interpolation by a piecewise quadratic smooth function is solved. A constructive procedure is provided.     

Spatial interpolation of large climate data sets using bivariate thin plate smoothing splines

Thin plate smoothing splines are widely used to spatially interpolate surface climate, however, their application to large data sets is limited by computational efficiency. Standard analytic calculation of thin plate smoothing splines requires O(n³) operations, where n is the number of data points, making routine computation infeasible for data sets with more than around 2000 data points. An O(N) iterative procedure for calculating finite element approximations to bivariate minimum generalised cross validation (GCV) thin plate smoothing splines operations was developed, where N is the number of grid points. The key contribution of the method lies in the incorporation of an automatic procedure for optimising smoothness to minimise GCV. The minimum GCV criterion is commonly used to optimise thin plate smoothing spline fits to climate data. The method discretises the bivariate thin plate smoothing spline equations using hierarchical biquadratic B-splines, and uses a nested grid multigrid procedure to solve the system. To optimise smoothness, a double iteration is incorporated, whereby the estimate of the spline solution and the estimate of the optimal smoothing parameter are updated simultaneously. When the method was tested on temperature data from the African and Australian continents, accurate approximations to analytic solutions were obtained.