Empirical likelihood for AR-ARCH models based on LAD estimation

By employing the empirical likelihood method,confidence regions for the stationary AR(p)-ARCH(q) models are constructed.A self-weighted LAD estimator is proposed under weak moment conditions.An empirical log-likelihood ratio statistic is derived and its asymptotic distribution is obtained.Simulation studies show that the performance of empirical likelihood method is better than that of normal approximation of the LAD estimator in terms of the coverage accuracy,especially for relative small size of observation.     

单分子层受限冰-水共存界线毛细波与固-液相变动力学

冰-水界面动力学性质在冰形核、生长、表界面熔化中扮演核心角色,长期以来一直被广泛关注.然而,受限水体系中冰-水界面的动力学性质却鲜有报道.本工作利用平衡态分子动力学模拟方法和受限固-液两相平衡模拟技术,对两种水模型(恒定偶极矩、可极化)描述的单分子层受限冰-水两相平衡体系中的一维固-液界线开展研究.通过对一维受限冰-水...     

提升钢丝绳动态分析的分段线性化解法

本文在研究提升机绳系动态特性过程中,建立了一类非齐次边界条件混合问题的波动方程;应用离散化方法将非齐次项分段线性化,得到了该类波动方程的半解析解.     

图的倍图与补倍图

计算机科学数据库的关系中遇到了可归为倍图或补倍图的参数和哈密顿圈的问题．对简单图C,如果V（D（G））：V（G）∪V（G′）E（D（G））=E（C）∪E（C″）U{vivj′｜vi∈V（G）,Vj′∈V（G′）且vivj∈E（G））那么,称D（C）是C的倍图,如果V（D（G））=V（C）∪V（G′）,E（D（C））：E（C）∪E（G′）∪{vivj′}vi∈V（G）,vj′∈V（G’）and vivj∈（G））,称D（C）是G的补倍图,这里G′是G的拷贝．本文研究了D（G）和D的色数,边色数,欧拉性,哈密顿性和提出了D（G） 的边色数是D（G）的最大度等公开问题．     

关于一类四阶非线性系统李雅普诺夫函数构造的研究

本文导出了四维线性系统的李雅普诺夫函数公式,研究了一类四阶非线性系统平凡解的稳定性。     

基于免疫记忆克隆算法的指数化投资组合优化构建策略

本文基于克隆选择学说及基于克隆选择学说及生物免疫响应过程的相关机理,提出用于指数化投资的免疫记忆克隆算法,并将其应用于指数化投资组合优化构建模型的求解,旨在探索指数化投资的优化构建策略。文章首先提出多目标的指数化投资组合构建模型。其次,分别设计了适用于指数化投资组合构建策略的抗原、抗体、亲和度函数、克隆选择算子、免疫记忆算子和相应的进化算法。该算法有效避免了传统遗传算法所存在的计算后期解的多样性差、易早熟以及收敛速度慢等缺点。同时,提出了限制投资组合中股票数量的启发式算法。最后,使用包括上证180指数在内的6组世界主要股票市场指数及其成份股的历史数据对模型及算法进行测算,结果表明算法具有良好的求解能力和收敛速度,所建模型的合理性和有效性亦被论证,模型和算法均具有很强的实践价值;     

Beurling-Ahlfors扩张的伸张函数与ID-同胚

本文研究实轴上同胚在上半平面的扩张.利用拟对称函数ρ对伸张函数D作了较精细的估计.同时借助于诱导的边界函数对D、ρ在边界附近的性质作了进一步刻划.作为应用,我们分别给出上半平面存在ID-同胚扩张和IID-同胚扩张的充分条件.     

Towards a unified recurrent neural network theory: The uniformly pseudo-projection-anti-monotone net

In the past decades, various neural network models have been developed for modeling the behavior of human brain or performing problem-solving through simulating the behavior of human brain. The recurrent neural networks are the type of neural networks to model or simulate associative memory behavior of human being. A recurrent neural network (RNN) can be generally formalized as a dynamic system associated with two fundamental operators: one is the nonlinear activation operator deduced from the input-output properties of the involved neurons, and the other is the synaptic connections (a matrix) among the neurons. Through carefully examining properties of various activation functions used, we introduce a novel type of monotone operators, the uniformly pseudo-projectionanti-monotone (UPPAM) operators, to unify the various RNN models appeared in the literature. We develop a unified encoding and stability theory for the UPPAM network model when the time is discrete. The established model and theory not only unify but also jointly generalize the most known results of RNNs. The approach has lunched a visible step towards establishment of a unified mathematical theory of recurrent neural networks.     

拟常曲率黎曼流形中子流形的几何不等式的一些注记

本文研究了拟常曲率黎曼流形中子流形的Chen不等式.利用代数技巧,建立了Chen广义不等式、Chen-Ricci不等式和关于卷积函数和平均曲率平方的不等式,推广了Özgür和Chen的一些结果.     

Observation of no significant surface enhancement of Raman scattering from benzoic acid molecules on mercury

There is controversy regarding the surface enhancement of Raman scattering due to liquid Hg, and the enhancement of benzoic acid molecules adsorbed on liquid Hg has been determined to be essentially zero. However, if the noise error bar is taken into account, the maximum enhancement is estimated to be less than 30. The same enhancement was observed when the temperature was reduced in solidifying liquid Hg.