B-值双随机Dirchlet级数的收敛性

主要研究了B-值双随机Dirichlet级数在不同条件(i){Xn}服从强大数定律,且 (ii){Xn}独立不同分布,且 等条件下的收敛性,得出了收敛横坐标的简洁公式．     

CONVERGENCE OF ONLINE GRADIENT METHOD WITH A PENALTY TERM FOR FEEDFORWARD NEURAL NETWORKS WITH STOCHASTIC INPUTS

Online gradient algorithm has been widely used as a learning algorithm for feedforward neural network training. In this paper, we prove a weak convergence theorem of an online gradient algorithm with a penalty term, assuming that the training examples are input in a stochastic way. The monotonicity of the error function in the iteration and the boundedness of the weight are both guaranteed. We also present a numerical experiment to support our results.     

受控AR模型中参数估计误差的一致有界性分析

本针对系统为受控AR模型，其参数估计采用随机梯度算法时，用鞅收敛定理的推广形式分析了它的收敛性，得到了参数估计误差一致有界的结果．     

两两正交拉丁方最大数目的新上界

记D（x）是使得TD（x，n）存在的最小的数．本文给出D（x）的一个上界．     

Asymptotics of Huber-Dutter Estimators for Partial Linear Model with Nonstochastic Designs

For partial linear model Y=X~τβ_0 _(g0)(T) εwith unknown β_0∈R~d and an unknown smooth function go, this paper considers the Huber-Dutter estimators of β_0, scale σfor the errors and the function go respectively, in which the smoothing B-spline function is used. Under some regular conditions, it is shown that the Huber-Dutter estimators of β_0 and σare asymptotically normal with convergence rate n~((-1)/2) and the B-spline Huber-Dutter estimator of go achieves the optimal convergence rate in nonparametric regression. A simulation study demonstrates that the Huber-Dutter estimator of β_0 is competitive with its M-estimator without scale parameter and the ordinary least square estimator. An example is presented after the simulation study.     

Strong convergence theorems for strictly asymptotically pseudocontractive mappings in Hilbert spaces

The purpose of this paper is by using CSQ method to study the strong convergence problem of iterative sequences for a pair of strictly asymptotically pseudocontractive mappings to approximate a common fixed point in a Hilbert space. Under suitable conditions some strong convergence theorems are proved. The results presented in the paper are new which extend and improve some recent results of Acedo and Xu [Iterative methods for strict pseudo-contractions in Hilbert spaces. Nonlinear Anal., 67(7), 2258??271 (2007)], Kim and Xu [Strong convergence of modified Mann iterations for asymptotically nonexpansive mappings and semigroups. Nonlinear Anal., 64, 1140??152 (2006)], Martinez-Yanes and Xu [Strong convergence of the CQ method for fixed point iteration processes. Nonlinear Anal., 64, 2400??411 (2006)], Nakajo and Takahashi [Strong convergence theorems for nonexpansive mappings and nonexpansive semigroups. J. Math. Anal. Appl., 279, 372??79 (2003)], Marino and Xu [Weak and strong convergence theorems for strict pseudocontractions in Hilbert spaces. J. Math. Anal. Appl., 329(1), 336??46 (2007)], Osilike et al. [Demiclosedness principle and convergence theorems for k-strictly asymptotically pseudocontractive maps. J. Math. Anal. Appl., 326, 1334??345 (2007)], Liu [Convergence theorems of the sequence of iterates for asymptotically demicontractive and hemicontractive mappings. Nonlinear Anal., 26(11), 1835??842 (1996)], Osilike et al. [Fixed points of demi-contractive mappings in arbitrary Banach spaces. Panamer Math. J., 12 (2), 77??8 (2002)], Gu [The new composite implicit iteration process with errors for common fixed points of a finite family of strictly pseudocontractive mappings. J. Math. Anal. Appl., 329, 766??76 (2007)].     

严格反馈非线性系统中参数收敛的系统化方法

研究严格反馈非线性系统中参数的收敛性.在适当的持续激励条件下,通过构造一个显式、全局的强Lyapunov函数,给出了系统参数估计收敛于真值的充分条件,且闭环信号全局一致有界,跟踪误差渐近收敛于零.仿真算例验证了设计方案的可行性和有效性.     

函数列一致收敛判别法

利用函数列和函数一致连续的有关性质,得到了函数列一致收敛新的判别法.     

一种新的自适应非单调牛顿算法（英文）

本文针对无约束优化问题,提出一种新的自适应非单调线搜索技术.基于新的非单调线搜索技术,提出一种自适应非单调牛顿算法.在适当的假设下,证明了新的算法的全局收敛性.数值结果表明了该算法的可行性和有效性.     

NOD随机变量阵列加权乘积和的完全收敛性

利用NOD随机变量的性质,研究了行为NOD随机变量阵列加权乘积和的完全收敛性,获得了一些新的结果,所得的结果推广和改进了已知的一些文献中的一系列结果.