K(o)the-Bochner空间的凸性

本文利用K(o)the函数空间的性质以及K(o)the函数空间与K(o)the-Bochner空间的关系,讨论了K(o)the-Bochner空间E(X)的凸性,主要结果如下:(a)给出E(X)的端点的充分条件,得到了E(X)严格凸的判据,相应地推广了Lp(μ,X)以及LΦ(X)的结果;(b)讨论了E(X)的弱局部一致凸和局部完全k-凸;(c)刻画了E(X)的强凸,给出了E(X)强凸的充要条件.     

Banach空间上有界可逆线性变换逆变换的结构

Fillmore在[1]中得到一个定理:设A,T是Banach空间X上的线性变换,A有界,若Lat(A) Lat(T)且AT=TA,则T是A的多项式.在本文里,以此作为引理,讨论了Banach空间上可逆线性变换A在什么情况下,A-1可表示为A的多项式.本文最主要的结论是定理3.4:设X是Banach空间,A是X上的有界线性变换,且可逆,则A-1是A的多项式当且仅当A-1是A的局部多项式.     

Orlicz-Sobolev空间的中点局部一致凸性

本文研究了Orlicz-Sobolev空间的中点局部一致凸性，通过结合Orlicz空间和Sobolev空间的技巧得到分别赋Luxemburg范数和赋Orlicz范数的Orlicz-Sobolev空间具有中点局部一致凸性的充要条件．     

加权二次有理Bézier插值曲线

在本文中,给定一组有序空间数据点列及每个数据点的切矢向量,利用加权二次有理Bézier曲线对数据点作插值曲线,使该曲线具有C1连续性,并且权因子只是对相应顶点曲线附近产生影响,同调整两个相邻的权因子可以调整这两个相邻顶点之间的曲线和它的控制多边形.     

The Iyengar Type Inequalities with Exact Estimations and the Chebyshev Central Algorithms of Integrals

In this paper, both low order and high order extensions of the Iyengar type inequality are obtained. Such extensions are the best possible in the same sense as that of the Iyengar inequality. hzrthermore, the Chebyshev central algorithms of integrals for some function classes and some related problems are also considered and investigated.     

风险中性过程的非参数估计

无套利定价理论说明，任何衍生证券定价都可以在基础资产价格(或收益)的风险中性过程基础上进行，而且方差函数的估计是估计风险中性过程的关键问题，由于方差不可观测，采用特定的参数模型将是危险的。本文主要讨论时间序列模型下条件方差函数的非参数估计，对核估计和局部多项式估计给出确定窗宽的M-图方法，并给出时间序列模型下衍生证券定价的风险中性调整方法，最后作了模拟计算。     

ON THE RATE OF WEIGHTED Lp CONVERGENCE BY THE GRUENWALD INTERPOLATORY OPERATORS

This paper gives the weighted Lp convergence rate estimations of the Gruenwald interpolatory polynomials based on the zeros of Chebyshev polymomials of the first kind,and proves that the order of the estimations is optimal for p≥1.     

多小波子空间上的单小波表示

本文在较弱的条件下,建立了2重多小波子空间与单小波子空间的关系.即由2重多小波构造出单小波.一方面,这种单小波的平移伸缩与2重多小波的平移伸缩生成的子空间是完全相同的;另一方面,它具有插值性.因此通过构造出的单小波建立了多小波子空间上的Shannon型采样定理.     

全景图像几何畸变校正的算法研究及其软件实现

对于360°视场的全景图像,本文提出了分别对其进行切向和径向畸变校正的处理算法,并用插值处理提高处理精度。达到了将全景图像恢复为常规视场平面像的目的。它是进一步图像分析的前期工作。     

Local Prediction of Chaotic Time Series Based on Support Vector Machine

Based on phase space delay-coordinate reconstruction of a chaotic dynamics system, we propose a local prediction of chaotic time series using a support vector machine （SVM） to overcome the shortcomings of traditional local prediction methods. The simulation results show that the performance of this proposed predictor for making onestep and multi-step prediction is superior to that of the traditional local linear prediction method and global SVM method. In addition, it is significant that its prediction performance is insensitive to the selection of embedding dimension and the number of nearest neighbours, so the satisfying results can be achieved even if we do not know the optimal embedding dimension and how to select the number of nearest neighbours.