空间有理参数多项式曲线的整数级绘制算法

讨论了空间有理曲线中心投影后导数上界的估计，基于曲线各阶差分的递推计算，给出了空间有理参数多项式曲线的快速绘制算法．算法只用到整数的加减法，效率高．     

LMS估计与岩性反演

该文介绍一类岩性反演问题．从理论上分析LMS估计的结构。在空间齐次假设下，得到不变矩阵α＝（αij）的递推计算关系．同时，建立一种计算机模拟岩性的算法，该算法被实践证明是有效的．     

无限时区最优控制系统的逼近

对于无限时区最优连续－转换－脉冲控制系统值函数满足的拟变分不等式，本文给出了其关于时间的离散化逼近系统，以及逼近解和原粘性解之间的估计．另外，相对于连续－转换控制系统，本文给出了其空间离散化方程，并且给出了解的估计以及求解的算法．     

PPS适应性整群抽样下估计量的研究

在群规模存在差异时,最有效的抽样方法是PPS整群抽样;在单元存在聚集性且总体信息不明确时,使用PPS适应性整群抽样是最有效的.利用PPS适应性整群抽样设计中的样本外推机制产生的样本信息,对HH和HT估计进行调整,提出修正的HH和HT估计,并对估计量的性质进行了研究.     

基于二重网格的定常Navier-Stokes方程的局部和并行有限元算法

对二维定常的不可压缩的Navier-Stokes方程的局部和并行算法进行了研究．给出的算法是多重网格和区域分解相结合的算法,它是基于两个有限元空间:粗网格上的函数空间和子区域的细网格上的函数空间．局部算法是在粗网格上求一个非线性问题,然后在细网格上求一个线性问题,并舍掉内部边界附近的误差相对较大的解．最后,基于局部算法,通过有重叠的区域分解而构造了并行算法,并且做了算法的误差分析,得到了比标准有限元方法更好的误差估计,也对算法做了数值试验,数值结果通过比较验证了本算法的高效性和合理性．     

结构经济序列分析的状态空间方法

本文讨论结构经济时间序列用状态空间模型进行分解处理的方法．在§１中综述结构时间序列的状态空间描述．§２中着重论述了将处理不完全数据的ＥＭ－算法应用于状态空间模型参数的极大似然估计．在§３中给出采用本文所述方法对一些我国宏观经济序列的计算实例．     

基于自适应极大后验估计的空间目标运动状态确定

提出一种新的基于自适应极大后验(AMAP)估计的空间目标运动状态确定方法,致力于削弱未知干扰对状态估计的不利影响.针对带有干扰的离散时间非线性随机系统设计了AMAP估计算法,采用高斯-牛顿优化方法实现极大后验(MAP)估计,通过模式切换和加权融合强化算法的自适应能力.基于理论分析导出了状态估计均方误差(MSE)的表达式,说明所提算法能够达到优于传统扩展卡尔曼滤波(EKF)和MAP估计算法的精度.以空间目标运动状态确定系统为例,通过蒙特卡洛仿真验证了AMAP估计算法的性能优势,不同条件下的对比研究表明,所提算法具备应对未知干扰的自适应能力,能够有效提升空间目标运动状态估计精度.     

L~1空间中第二类Fredholm积分方程的投影数值解法

在L~1空间中对第二类Fredholm积分方程进行均值投影,利用先验估计和后验估计来进行误差估计,数值算例进一步验证了算法的合理性和有效性,体现了均值投影算法的优越性.     

一般状态空间模型的延迟取样估计方法

本文提出了一种用于处理一般状态空间模型状态估计的延迟取样估计算法. 数值实验的结果表明, 在计算复杂度相当的条件下, 该算法能够取得比同步估计算法更加精确的估计结果.     

交通事故与汽车驾驶员身体素质的相关性研究

本文首先定性描述了汽车驾驶员必须具备的身体素质，然后用多元统计分析方法对测试结果进行统计分析．文中提出的方法可供制定汽车驾驶员职业适应性标准参考．     

特征值问题的自适应反迭代有限元算法

1引言设Ω∈R~2为Lipschitz单连通的有界闭区域,X为定义在Ω的Sobolev空间,a(·,·)和b(·,·)为X×X→C的有界双线性或半双线性泛函,考虑变分特征值问题:求(λ,u≠0)∈C×X使得a(u,v)=λb(u,u),(?)u∈X,其中a(·,·)满足X上的"V-强制性"条件或者连续的inf-sup条件,设M_h为Q区域上的正则三角形剖分,X_h∈X为定义在M_h有限元子空间,上述变分问题对应的有限元离散问题为:求(λ_h,u_h)∈R×X,u_h≠0使得     

Modeling of memristor-based chaotic systems using nonlinear Wiener adaptive filters based on backslash operator

Memristor-based chaotic systems have complex dynamical behaviors, which are characterized as nonlinear and hysteresis characteristics. Modeling and identification of their nonlinear model is an important premise for analyzing the dynamical behavior of the memristor-based chaotic systems. This paper presents a novel nonlinear Wiener adaptive filtering identification approach to the memristor-based chaotic systems. The linear part of Wiener model consists of the linear transversal adaptive filters, the nonlinear part consists of nonlinear adaptive filters based on the backslash operator for the hysteresis characteristics of the memristor. The weight update algorithms for the linear and nonlinear adaptive filters are derived. Final computer simulation results show the effectiveness as well as fast convergence characteristics. Comparing with the adaptive nonlinear polynomial filters, the proposed nonlinear adaptive filters have less identification error.     

Expected hitting times for Backtracking Adaptive Search

This article analyses a counting process associated with a stochastic process arising in global optimisation. Backtracking adaptive search (BAS) is a theoretical stochastic global optimisation algorithm modelling the temporary acceptance of solutions of lower quality. BAS generalises the pure adaptive search and hesitant adaptive search algorithms, whose full search duration distributions are known. This article gives the exact expected search duration for backtracking adaptive search.     

Low-gain adaptive stabilization of infinite-dimensional second-order systems

In this paper, low-gain adaptive stabilization of infinite-dimensional undamped second-order systems is considered in the case where the input and output operators are collocated. The systems have an infinite number of poles and zeros on the imaginary axis. The adaptive stabilizer is constructed by a low-gain adaptive PI controller (proportional plus integral controller). An energy-like function and a multiplier function are introduced and low-gain adaptive stabilization of the linear second-order systems is analyzed.     

Adaptive protocols for parallel discrete event simulation

This paper reviews issues concerning the design of adaptive protocols for parallel discrete event simulation (PDES). The need for adaptive protocols are motivated in the background of the classical synchronisation problem that has driven much of the research in this field. Traditional conservative and optimistic protocols and their hybrid variants—that form the basis of adaptive protocols—are also discussed. Adaptive synchronisation protocols are reviewed with special reference to their characteristics regarding the aspects of the simulation state that influence the adaptive decision and the control parameters used. Finally, adaptive load management strategies and their relationship to the synchronisation protocol are discussed.     

Iterative implementation of the adaptive regularization yields optimality

The adaptive regularization method is first proposed by Ryzhikov et al. for the deconvolution in elimination of multiples. This method is stronger than the Tikhonov regularization in the sense that it is adaptive, i.e. it eliminates the small eigenvalues of the adjoint operator when it is nearly singular. We will show in this paper that the adaptive regularization can be implemented iterately. Some properties of the proposed non-stationary iterated adaptive regularization method are analyzed. The rate of convergence for inexact data is proved. Therefore the iterative implementation of the adaptive regularization can yield optimality.     

基于红利贴现的适应性预期股价决定模型研究

介绍了基于红利贴现的适应性预期股价模型,对适应性预期股价决定模型和理性预期股价决定模型进行了比较,并分析总结了已有的实证结果,得出了适应性预期模型对于成熟股票市场有很强解释能力并将对我国股市有借鉴意义的结论.     

Convergence of simple adaptive Galerkin schemes based on h − h/2 error estimators

We discuss several adaptive mesh-refinement strategies based on (h − h/2)-error estimation. This class of adaptive methods is particularly popular in practise since it is problem independent and requires virtually no implementational overhead. We prove that, under the saturation assumption, these adaptive algorithms are convergent. Our framework applies not only to finite element methods, but also yields a first convergence proof for adaptive boundary element schemes. For a finite element model problem, we extend the proposed adaptive scheme and prove convergence even if the saturation assumption fails to hold in general.     

Least‐squares finite element method on adaptive grid for PDEs with shocks

We apply the least‐squares finite element method with adaptive grid to nonlinear time‐dependent PDEs with shocks. The least‐squares finite element method is also used in applying the deformation method to generate the adaptive moving grids. The effectiveness of this method is demonstrated by solving a Burgers' equation with shocks. Computational results on uniform grids and adaptive grids are compared for the purpose of evaluation. The results show that the adaptive grids can capture the shock more sharply with significantly less computational time. For moving shock, the adaptive grid moves correctly with the shock. © 2005 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2006     

Adaptive quantile regression with precise risk bounds

An adaptive local smoothing method for nonparametric conditional quantile regression models is considered in this paper. Theoretical properties of the procedure are examined. The proposed method is fully adaptive in the sense that no prior information about the structure of the model is assumed. The fully adaptive feature not only allows varying bandwidths to accommodate jumps or instantaneous slope changes, but also allows the algorithm to be spatially adaptive. Under general conditions, precise risk bounds for homogeneous and heterogeneous cases of the underlying conditional quantile curves are established. An automatic selection algorithm for locally adaptive bandwidths is also given, which is applicable to higher dimensional cases. Simulation studies and data analysis confirm that the proposed methodology works well.