关于多元Szaesz—Mirakjan算子的加权逼近

本文主要讨论一类二元Ｓｚａｅｓｚ－Ｍｉｒａｋｊａｎ算子的加权逼近问题，我们首先指出了在通常的加权范数下它是无界的，然后我们给出了一类新的加权范数，在此范数下，它是有界的，最后利用一类多元Ｋ－泛函与多元分解技巧，我们给出了二元Ｓｚａｅｓｚ－Ｍｉｒａｋｊａｎ算子加权逼近的特征刻划。     

某些多元线性正算子的加权逼近

本文首先给出了在Ｌｐ逼近意义下某些线性正算子加Ｊａｃｏｂｉ权逼近时的特征定理，作为应用，我们给出了多元Ｂａｓｋａｋｏｖ型算子、多元Ｓｚａｓｚ－Ｍｉｒａｋｊａｎ型算子和多元Ｂｅｔａ算子加权逼近时的特征刻划．     

关于多元Baskakov算子的加权逼近

本文首先指出一类多元Ｂａｓｋａｋｏｖ算子在通常的加权范数下是无界的．然后给出了一类新的加权范数,在此范数下它是压缩的．最后利用多元分解技巧,解决了多元Ｂａｓｋａｋｏｖ算子加权逼近的特征刻划文问题．     

一类Bernstein型算子加权逼近

本文首先给出了一类用递归法定义的Ｂｅｒｎｓｅｉｎ型算子在一致逼近意义下的特征刻划,然后指出在通常的加权范数下,它虹无界的,通过引入的一种新范数,我们给出了该算子加Ｊａｃｏｂｉ权逼近的特征刻划。     

关于Szász-Mirakjan—Durrmeyer算子在Besov空间上的逼近阶

本文主要给出了Ｓｚａｓｚ－Ｍｉｒａｋｊａｎ－Ｄｕｒｒｍｅｙｅｒ算子在Ｂｅｓｏｖ空间上逼近阶的特征刻划     

Baskakov算子加权逼近的收敛阶

本文讨论了Ｂａｓｋａｋｏｖ算子加Ｊａｃｏｂｉ权逼近的收敛性，首先指出了按通常的加权范数，Ｂａｓｋａｋｏｖ算子是无界的。然后引入一种新的范数，在此范数下Ｂａｓｋａｋｏｖ算子具有压缩性，最后借助于Ｋ－泛函，我们着重讨论了它的特征刻划问题。     

Szasz型算子同时逼近的点态结果

本文给出了Ｓｚａｓｚ－ｉｒａｋｊａｎ算子和Ｓｚａｓｚ－Ｋａｎｔｏｒｏｖｉｃｈ算子组合的同时逼近的点态结果，并用其导数给出了高阶Ｌｉｐｓｃｈｉｔｚ函数类的特征刻划。     

关于Baskakov—Durrmeyer算子的一致逼近

本文首先给出了Ｂａｓｋａｋｏｖ－Ｄｕｒｒｍｅｙｅｒ算子一致逼近意义下的正定理，并把它推广到一类线性组合的情形，然后讨论了它的导数与光滑模的等价关系，最后给出了二元Ｂａｓｋａｋｏｖ－Ｄｕｒｒｍｅｙｅｒ算子逼近阶的特殊刻画。     

关于Baskakov－Kurrmeyer算子的一致逼近

本文首先给出了Ｂａｓｋａｋｏｖ－Ｄｕｒｒｍｅｙｅｒ算子在一致副近意义下的正定理，并把它推广到一类线性组合的情形，然后讨论了它的导数与光滑模的等价关系，最后给出了二元Ｂａｓｋａｋｏｖ－Ｄｕｒｒｍｅｙｅｒ算子逼近阶的特征刻画．     

单纯形上加权K—泛函与光滑模的等价性及其应用

本文首先讨论了高维单纯形上一类加权Ｋ－泛函与光滑模的等价性。然后作为应用，给出了高维单纯形上多元Ｂｅｒｎｓｔｅｉｎ算子加权逼近的特征刻划。     

基于精度的属性约简新方法

自Pawlak提出粗糙集概念以来,人们就一直对粗糙集的近似精度很感兴趣,出现了不少有关近似精度的文献.在粗糙集理论中,精度是量化由粗糙集边界引起的不精确性的一种重要数字特征.在分析传统精度和基于等价关系图的过剩熵的近似精度的基础上,提出了一种新的精度定义.比较发现,新定义的精度更具有合理性.同时把这个新定义的精度运用到了属性约简上,通过实例比较发现,本文提出的属性约简更具有可行性.     

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.     

MODIFIED NEWTON AND SECANT METHODS FOR SOLVING AN ORDER O(h~4) FINITE-DIFFERENCE PROBLEM

1IntroductionSolution0fn0nlineartwo-pointb0undaryvaIuepr0blems(NBVP)canoftenbefoundbythefinite-differenceappr0ach,wheref(t,y)isaconti-nuousfunction.Collatz[1]firstpresentedanapproximation0ffourthorderfwherey=(y1,''tyN)',g=(g1,'-,gN)'andtherelativepaperscanals0beseenin[2].Toestablishthesolutionof(1.l),thef0llowingmethodscanbeusedfnonlinearsuccessiverelaxati0n(NSOR)method[3],thedifferenceNewt0nmethod(0rNewtonmethod)[4],therelativesparsenonlinearequationpr0blemscanals0beseenin[5-8]-lnthisp…     

Adaptive Cross Approximation of Multivariate Functions

In this article we present and analyze a new scheme for the approximation of multivariate functions (d=3,4) by sums of products of univariate functions. The method is based on the Adaptive Cross Approximation (ACA) initially designed for the approximation of bivariate functions. To demonstrate the linear complexity of the schemes, we apply it to large-scale multidimensional arrays generated by the evaluation of functions.     

最佳混合范数逼近

最佳混合范数逼近@王建东...     

一个基于模糊随机集的粗糙近似算子的性质及应用

利用贴近度（或相似度）N（B，A）提出了模糊随机近似空间里的一种基于模糊随机集的粗糙近似算子，讨论了该种近似算子的一些主要性质；成功地探讨其在Fuzzy模式识别中的应用；最后给出了具体的例子说明了该算子用于Fuzzy模式识别的可行性。     

Upper and lower approximation models in interval regression using regression quantile techniques

In this paper, we propose new interval regression analysis based on the regression quantile techniques. To analyze a phenomenon in a fuzzy environment, we propose two interval approximation models. Without using all data, we first identify the main trend from the designated proportion of the given data. To select the main part of data to be analyzed, we introduce the regression quantile techniques. The obtained model is not influenced by extreme points since it is formulated from the center-located main proportion of the given data. After that, the interval regression model including all data can be identified based on the acquired main trend. The obtained interval regression model by the main proportion of the given data is called the lower approximation model, while interval regression model by all data is called the upper approximation model for the given phenomenon. Also it is shown that, from the lower approximation model (main trend) and the upper approximation model, we can construct a trapezoidal fuzzy model. The membership function of this fuzzy model is useful to obtain the locational information for each observation. The characteristic of our approach can be described as obtaining the upper and lower approximation models and combining them to be a fuzzy model for representing the given phenomenon in a fuzzy environment.     

基于D.C.分解的一类箱型约束的非凸二次规划的新型分支定界算法

提出了一类求解带有箱约束的非凸二次规划的新型分支定界算法.首先,把原问题目标函数进行D.C.分解(分解为两个凸函数之差),利用次梯度方法,求出其线性下界逼近函数的一个最优值,也即原问题的一个下界.然后,利用全局椭球算法获得原问题的一个上界,并根据分支定界方法把原问题的求解转化为一系列子问题的求解.最后,理论上证明了算法的收敛性,数值算例表明算法是有效可行的.     

广义对称正则长波方程的守恒差分格式

文章考虑了具有齐次边界条件的广义对称正则长波方程的有限差分格式.提出了一个守恒并且线性非耦合的三层有限差分格式,由于格式在计算中只需要解三对角线性方程组,从而避免了其中的迭代计算.文中先讨论了一个离散守恒量,然后我们利用离散泛函分析方法证明了格式的收敛性和稳定性,从理论上得到了收敛阶为O(h~2+τ~2).通过数值试验表明,所提的方法是可靠有效的.     

Approximations by linear operators in spaces of fuzzy continuous functions

In this work, we further develop the Korovkin-type approximation theory by utilizing a fuzzy logic approach and principles of neoclassical analysis, which is a new branch of fuzzy mathematics and extends possibilities provided by the classical analysis. In the conventional setting, the Korovkin-type approximation theory is developed for continuous functions. Here we extend it to the space of fuzzy continuous functions, which contains a great diversity of functions that are not continuous. Furthermore, we give several applications, demonstrating that our new approximation results are stronger than the classical ones.