广义权函数的最大类逼近及其应用

本文研究了广义权函数的最大类逼近问题,给出了K-最大类和A-最大类的特征刻划．同时也给出了关于广义权函数逼近的唯一性元特征刻划的结果和对某些具体逼近问题的应用,从而推广和改进了他人的结果．     

双材料界面裂纹平面问题的半权函数法

应用半权函数法求解双材料界面裂纹的平面问题．由平衡方程、应力应变关系、界面的连续条件以及裂纹面零应力条件推导出裂尖的位移和应力场,其特征值为lambda及其共轭．设置特征值为lambda的虚拟位移和应力场,即界面裂纹的半权函数A·D2由功的互等定理得到应力强度因子KⅠ和KⅡ以半权函数与绕裂尖围道上参考位移和应力积分关系的表达式．数值算例体现了半权函数法精度可靠、计算简便的特点．     

效用关联分析在决策分析中的应用

本文分析了灰色关联分析法的不足之处，提出了运用效用函数改进白化权函数的方法．实践证明，这种方法科学合理，适用性更强．     

带裂纹三点弯曲试样的动态应力强度因子分析

提出了计算带单边裂纹三点弯曲试样动态应力强度因子的新方法．首先由权函数的普遍形式和两种参考载荷下的应力强度因子,得到了带单边裂纹三点弯曲试样的权函数,然后考虑试样的转动惯性和剪切变形,根据振动理论推导出无裂纹梁内的动应力响应和分布,最后由权函数的思想推导出了带裂纹三点弯曲试样动态应力强度因子公式．通过有限元数值计算,验证了该方法的正确性,结果比较表明公式具有较高的精度．另外,还研究了冲击载荷下三点弯曲试样的动态应力强度因子随裂纹长度和加载速率的变化规律．     

关于B－值强平稳相依随机变量列一类小参数级数的渐近性

迄今为止，人们在对Ｂ－值强平稳相依随机变量列的小参数级数的渐近性的研究中，仅涉及到一些较特殊的拟权函数及边界函数，本文则对一类十分广泛的拟权函数及边界函数得出了相应的结果，从而推出了一系列有趣的事实．     

R上分段单调和分段加倍的加倍权

该文研究了R上几类权函数为加倍权的条件.首先给出了R上单调权函数为加倍权的充要条件;其次刻画了R上分段单调权函数为加倍权的条件;最后讨论了R上分段加倍权函数为加倍权的条件.     

关于一个非齐次核的Hilbert型积分算子

应用权函数的方法及实分析与泛函分析的思想技巧,定义了一个非齐次核的Hilbert型积分算子,并求出其联系范数的两个等价不等式．作为应用,还考虑了其逆形式及一些特殊核的例子．     

带有不同权函数的退化拟线性椭圆方程弱解的Holder正则性

我们研究了一类系数依赖于两不同权函数的退化拟线性椭圆方程．利用加权Sobolev不等式,加权Poincare不等式及Moser迭代技巧,得到了非负弱解的Harnack不等式,证明了弱解的H61der连续性．     

误差为鞅差序列的回归函数估计的收敛速度

当误差为鞅差序列时,研究固定设计点列情形下非参数回归函数一般权函数的非参数估计,并在一些基本条件下给出了估计的一致最优强收敛速度．     

一个-4齐次核的Hardy-Hilbert型积分不等式

以Hilbert不等式为代表的双线型不等式是分析学的重要不等式。近代利用权函数方法,该类不等式的推广应用研究得到深入的发展。本文应用权函数的方法,建立了一个具有最佳常数因子的-4齐次核的双线型不等式,作为应用,导出其等价的不等式。     

Nonlinear approximation using Gaussian kernels

It is well known that nonlinear approximation has an advantage over linear schemes in the sense that it provides comparable approximation rates to those of the linear schemes, but to a larger class of approximands. This was established for spline approximations and for wavelet approximations, and more recently by DeVore and Ron (in press) [2] for homogeneous radial basis function (surface spline) approximations. However, no such results are known for the Gaussian function, the preferred kernel in machine learning and several engineering problems. We introduce and analyze in this paper a new algorithm for approximating functions using translates of Gaussian functions with varying tension parameters. At heart it employs the strategy for nonlinear approximation of DeVore-Ron, but it selects kernels by a method that is not straightforward. The crux of the difficulty lies in the necessity to vary the tension parameter in the Gaussian function spatially according to local information about the approximand: error analysis of Gaussian approximation schemes with varying tension are, by and large, an elusive target for approximators. We show that our algorithm is suitably optimal in the sense that it provides approximation rates similar to other established nonlinear methodologies like spline and wavelet approximations. As expected and desired, the approximation rates can be as high as needed and are essentially saturated only by the smoothness of the approximand.     

Interpolating Cubic Spline Wavelet Packet on Arbitrary Partitions

In many applications, the splines on an arbitrary partition are very useful. In this paper, a spline wavelet structure is created in the way that it provides a multiresolution approximation of the spline subspaces with arbitrary partition in the space of continuous functions on a finite interval. Based on the wavelet basis and the wavelet packet in this structure, a multi-level interpolation method is developed for decomposing a function into wavelet series and reconstructing it from its wavelet representation.     

Asymptotic error expansion of wavelet approximations of smooth functions II

Summary. We generalize earlier results concerning an asymptotic error expansion of wavelet approximations. The properties of the monowavelets, which are the building blocks for the error expansion, are studied in more detail, and connections between spline wavelets and Euler and Bernoulli polynomials are pointed out. The expansion is used to compare the error for different wavelet families. We prove that the leading terms of the expansion only depend on the multiresolution subspaces and not on how the complementary subspaces are chosen. Consequently, for a fixed set of subspaces , the leading terms do not depend on the fact whether the wavelets are orthogonal or not. We also show that Daubechies' orthogonal wavelets need, in general, one level more than spline wavelets to obtain an approximation with a prescribed accuracy. These results are illustrated with numerical examples. Received May 3, 1993 / Revised version received January 31, 1994     

Quintic nonpolynomial spline solutions for fourth order two-point boundary value problem

In this paper, we develop quintic nonpolynomial spline methods for the numerical solution of fourth order two-point boundary value problems. Using this spline function a few consistency relations are derived for computing approximations to the solution of the problem. The present approach gives better approximations and generalizes all the existing polynomial spline methods up to order four. This approach has less computational cost. Convergence analysis of these methods is discussed. Two numerical examples are included to illustrate the practical usefulness of our methods.     

最小支集样条小波有限元

本文认真分析研究了最小支集样条小波及其有关性质,用以张量积形式构造的二维小波建立了最小支集样条小波插值函数,讨论了其相关的性质,随后用最小支集样条小波有限元法去解弹性薄板小挠度问题,给出了数值解的误差阶,最后列举了一个数值例子．     

Splines and Linear Control Theory

In this work, the relationship between splines and the linear control theory has been analyzed. We show that spline functions can be constructed naturally from the control theory. By establishing a framework based on control theory, we provide a simple and systematic way to construct splines. We have constructed the traditional spline functions including polynomial splines and the classical exponential spline. We have also discovered some new spline functions such as the combination of polynomial, exponential and trigonometric splines. The method proposed in this paper is easy to implement. Some numerical experiments are performed to investigate properties of different spline approximations.     

Foveal detection and approximation for singularities

Projections in a foveal space at u approximate functions with a resolution that decreases proportionally to the distance from u. Such spaces are defined by dilating a finite family of foveal wavelets, which are not translated. Their general properties are studied and illustrated with spline functions. Orthogonal bases are constructed with foveal wavelets of compact support and high regularity. Foveal wavelet coefficients give pointwise characterization of nonoscillatory singularities. An algorithm to detect singularities and choose foveal points is derived. Precise approximations of piecewise regular functions are obtained with foveal approximations centered at singularity locations.     

Wavelet improvement in turning point detection using a hidden Markov model: from the aspects of cyclical identification and outlier correction

The hidden Markov model (HMM) has been widely used in regime classification and turning point detection for econometric series after the decisive paper by Hamilton (Econometrica 57(2):357–384, 1989). The present paper will show that when using HMM to detect the turning point in cyclical series, the accuracy of the detection will be influenced when the data are exposed to high volatilities or combine multiple types of cycles that have different frequency bands. Moreover, outliers will be frequently misidentified as turning points. The present paper shows that these issues can be resolved by wavelet multi-resolution analysis based methods. By providing both frequency and time resolutions, the wavelet power spectrum can identify the process dynamics at various resolution levels. We apply a Monte Carlo experiment to show that the detection accuracy of HMMs is highly improved when combined with the wavelet approach. Further simulations demonstrate the excellent accuracy of this improved HMM method relative to another two change point detection algorithms. Two empirical examples illustrate how the wavelet method can be applied to improve turning point detection in practice.     

Iterative methods based on spline approximations to detect discontinuities from Fourier data

Recently, spline approximations have been proposed for the reconstruction of piecewise smooth functions from Fourier data. That approach makes possible to retrieve the functions from their Fourier coefficients for any given degree of accuracy when the discontinuity points are known. In this paper we present iterative methods based on those spline approximations, for several degrees, to find locations and amplitudes of the jumps of a piecewise smooth function, given its Fourier coefficients. We also present numerical experiments comparing with different previous approaches.     

Integration modified wavelet neural networks for solving thin plate bending problem

In this paper, a modified wavelet neural network (MWNN), which is trained by chaos particle swarm optimization and whose activation function is fourth-order scaling function of spline wavelet, is first proposed for solving thin plate bending problem. The highest derivatives of variables in the governing equations are represented by the outputs of MWNN. The variables and the other derivatives are obtained by integrated outputs of MWNN. During the integration process, multiple boundary conditions can be implemented straightforward. It has been verified that the MWNN method can successfully solve various thin plate bending problems and it is convergent based on different distributions of scattered points.