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Stability and convergence of the model are proven by means of the weak convergence theorem of generalized function and the convolved integration theory.

由广义函数弱收敛定理和卷积理论,证明所提出的非局部连续模型具备收敛性和稳定性。

This project also obtained several limit theorems for some important dependent random variables and stochastic processes, such as the Strassen law of the iterated logarithm for negatively dependent random variables, strong limit theorems for mixing random vectors in Banach spaces, sample path properties for two-parameter fractional Wiener processes, and so on.

随机环境中的随机变量与随机过程的研究在国内外相当活跃,本项目主要研究它们的极限性质,着重研究了随机风景中随机变量与随机过程的极限性质,主要取得了以下几个结果:首先对简单对称的Kesten-Spitzer随机游动在低阶矩的条件下给出了强逼近,大大减弱了前人要求任意阶矩的条件,然后对独立风景中的一般随机变量给出了强逼近的一般性结果,由此导出在风景和随机变量都只具有低阶矩的条件下的独立但不同分布、混合相依变量的强逼近,在只有弱高于二阶矩的条件下得到了重相对数律和弱收敛;给出了连续时间参数的Brown风景中Brown运动和稳定风景中稳定过程的滞后增量和连续模等精确样本轨道性质;同时给出了一些重要的相依随机变量和过程的若干极限定理,如负相关随机变量的Strassen重对数律、抽象空间上混合相依变量的一些强极限定理成立的充分必要条件、两参数分数Wiener过程的样本轨道性质等。

Some kinds of limiting properties of the linear processes under various dependence assumptions are discussed in this paper.

本文的第一章就线性过程弱收敛方面的结果进行了深入的讨论。

The monotonicity and the convergence of deterministic nature are proved.

文中给出了误差函数的单调性定理以及该算法的弱收敛和强收敛性定理。

Compactness and the relation between strong convergence and weak* convergence.

紧性以及强收敛和弱收敛之间的关系。

The asymptotic unbias edness and the strong consistency and rate of convergence under given conditions are obtained.

在测度弱收敛的意义下,研究了一般概率测度μ的核估计μn,得到了它的渐近无偏性、强相合性及在给定条件下的收敛速度。

As an application of continuous wavelet transform,we discuss the relationsbetween some differemtial equaltions and the integral equations by using thecontinuous wavelet transform in 〓,vector function space andabstract function space respectively;prove that they are equivalent not only in theweak topology but also in the strong topology.

作为连续小波变换的应用,分别利用〓上的,多元函数空间上的,向量函数空间上的和抽象函数空间上的连续小波变换分别得到了某些线性微分方程,某些线性偏微分方程,某些向量线性微分方程和某些抽象函数的微分方程分别等价于其相应的积分方程,证明了它们不仅在弱收敛意义下而且在范数收敛意义下是等价的。

The objective of Chapter 2 is to give the weak convergence theorem of S ={T1 :t E G of asymptotically nonexpansive mappings in a uniformly convex Banach space without assuming that X has a Frechet differentiable norm.

本文第二章在G仅要求是一个定向网,X为不具有范数Frechet可微条件的一致凸Banach空间的情况下,给出了一族渐近非扩张映射的弱收敛定理。

By estimates of the spectral gap, Sobolev inequality and the weak convergence theory of stochastic process, we obtain that the annealing process converges in probability to a neighbourhood of the global minimal set of the potential function.

利用谱隙估计和Sobolev不等式方法以及随机过程的弱收敛理论我们得到这种退火过程依概率收敛到势函数的全局最小点附近。

The existence of the solution we have defined is proved by regularized method and weakly convergent technique.

我们所定义的弱解的存在性是通过正则化方法以及弱收敛技巧得到的。

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