全局最优信息融合估计算法(Ⅱ)

假定局部滤波器的状态估计是相关的条件下，得到了状态向量的全局最优融合估计，作为应用，得到了比文[1]中更为普遍，实用的结果。     

基于分层抽样的校准方法分别比率-乘积估计研究

比率估计在抽样估计阶段利用辅助信息,提高了估计量的估计精度,是抽样调查中一类较为常用的估计方法,但现有的一些比率估计方法均具有各自的最优条件,这在一定程度上影响了它们在实际调查中的应用。为了解决比率估计的最优限制问题,本文引入了校准估计方法,并基于分层抽样研究了总体均值的校准方法分别比率-乘积估计量。在大样本情况下,本文推导了新估计量的估计偏差和均方误差,说明新估计量具有渐近无偏性,并在估计量均方误差最小时,得到了总体参数的渐近最优估计量和渐近最优估计量的方差。在模拟研究中,根据比率估计量的最优条件是否满足,本文生成了两种不同的总体,对比分析了新估计量和现有比率估计量的估计效果,结果表明在两种不同的情况下,新估计量的估计效果均优于现有估计量的估计效果。最后,本文利用一个实际例子,验证了新估计量的有效性和实用性。     

多传感器分布式检测和估计融合

随着科学和工业的发展,信息科学领域涌现出许多应用数学科学问题,这些问题的研究拓宽和加速了基础数学理论的研究.另一方面,基础数学理论的进展以及在信息科学领域的成功应用也促使了工业领域的蓬勃发展.数学与其他学科的相互渗透已成为当今应用数学的主要特点之一,如在2010年ICM(国际数学家大会)上有2个分别关于控制和图像科学的1小时大会报告、7个关于"Mathematical Aspects of Computer Science"的特邀报告和8个关于"Mathematics in Science and Technology"的特邀报告.多源信息融合是数学和信息科学结合的一个重要研究方向.本文主要考虑其中两类基本问题:多传感器检测融合和估计融合,主要创新点包括:(1)对一般传感器观测相关条件下的多传感器分布式检测融合系统,获得同时搜索最优传感器律和最优融合律的高效算法;(2)对失序观测、错误观测和异步观测多传感器分布式估计融合系统,给出统一的全航迹(不仅仅校正当前状态估计)全局最优估计融合公式和算法;(3)对有偏不确定系统估计融合提出极小化Euclid误差准则,并基于多传感器和多算法估计融合获得极小化Euclid误差的抗偏估计高效算法.     

线性常微分方程的全局误差估计和优化求解方法

线性常微分方程初值问题求解在许多应用中起着重要作用.目前,已存在很多的数值方法和求解器用于计算离散网格点上的近似解,但很少有对全局误差(global error)进行估计和优化的方法.本文首先通过将离散数值解插值成为可微函数用来定义方程的残差;再给出残差与近似解的关系定理并推导出全局误差的上界;然后以最小化残差的二范数为目标将方程求解问题转化为优化求解问题;最后通过分析导出矩阵的结构,提出利用共轭梯度法对其进行求解.之后将该方法应用于滤波电路和汽车悬架系统等实际问题.实验分析表明,本文估计方法对线性常微分方程的初值问题的全局误差具有比较好的估计效果,优化求解方法能够在不增加网格点的情形下求解出线性常微分方程在插值解空间中的全局最优解.     

随机设计变量情形回归函数的非线性小波估计 *

在随机设计变量情形 ,构造了回归函数的非线性小波估计以及自适应非线性小波估计 .证明了非线性小波估计在Besov空间中可达到最优收敛速度 ,自适应非线性小波估计在一大类Besov空间中可达到次最优收敛速度 ,即和最优收敛速度只相差lnn .这样 ,在随机设计变量情形 ,所构造的回归函数的非线性小波估计和在固定设计点下对回归函数所构造的非线性小波估计几乎具有相同的优良性质 .进一步 ,只要求误差有有界三阶矩 ,而不要求误差服从正态分布 .     

矩形域上散乱数据多元最优插值的误差估计及其超收敛性（上）：带连续…

本文我们讨论了矩形域上带连续边界条件的一类多元散乱数据最优插值，给出了某些情形插值的误差估计，误差估计表明在某些点上还具有超收敛性。     

带有不等式约束的非线性规划问题的一个精确增广Lagrange函数

对求解带有不等式约束的非线性非凸规划问题的一个精确增广Lagrange函数进行了研究．在适当的假设下,给出了原约束问题的局部极小点与增广Lagrange函数,在原问题变量空间上的无约束局部极小点之间的对应关系．进一步地,在对全局解的一定假设下,还提供了原约束问题的全局最优解与增广Lagrange函数,在原问题变量空间的一个紧子集上的全局最优解之间的一些对应关系．因此,从理论上讲,采用该文给出的增广Lagrange函数作为辅助函数的乘子法,可以求得不等式约束非线性规划问题的最优解和对应的Lagrange乘子．     

基于最优估计的数据融合理论

本文提出了一种最优加权的数据融合方法，分析了最优权值的分配原则；给出了多源信息统一的线性融合模型，使其表示不受数据类型和融合系统结构的限制，并指出在噪声协方差阵正定的前提下，线性最小方差估计融合和加权最小二乘估计融合是等价的；介绍了数据融合中的Bayes极大后验估计融合方法，给出了利用极大后验法进行传感器数据融合的一般表示公式；最后以两传感器数据融合为例，证明了利用Bayes极大后验估计进行两传感器数据融合所得到的融合状态的精度比相同条件下极大似然估计得到的精度要高，同时它们均优于任一单传感器局部估计精度。     

基于单纯形方法的双层线性规划全局优化算法

通过分析双层线性规划可行域的结构特征和全局最优解在约束域的极点上达到这一特性,对单纯形方法中进基变量的选取法则进行适当修改后,给出了一个求解双层线性规划局部最优解方法,然后引进上层目标函数对应的一种割平面约束来修正当前局部最优解,直到求得双层线性规划的全局最优解.提出的算法具有全局收敛性,并通过算例说明了算法的求解过程.     

问题变分不等式的一类各向异性Crouzeix-Raviart型有限元逼近

本文研究了Signorini变分不等式问题的一类各向异性Crouzeix-Raviart型非协调有限元逼近。通过一些新的技巧,得到了相应的最优误差估计。     

A modified DNA genetic algorithm for parameter estimation of the 2-Chlorophenol oxidation in supercritical water

Based on the mechanism of biological DNA genetic information and evolution, a modified DNA genetic algorithm (MDNA-GA) is proposed to estimate the kinetic parameters of the 2-Chlorophenol oxidation in supercritical water. In this approach, DNA encoding method, choose crossover operator and frame-shift mutation operator inspired by the biological DNA are developed for improving the global searching ability. Besides, an adaptive mutation probability which can be adjusted automatically according to the diversity of population is also adopted. A local search method is used to explore the search space to accelerate the convergence towards global optimum. The performance of MDNA-GA in typical benchmark functions and kinetic parameter estimation is studied and compared with RNA-GA. The experimental results demonstrate that the proposed algorithm can overcome premature convergence and yield the global optimum with high efficiency.     

Stochastic comparison algorithm for continuous optimization with estimation

The problem of stochastic optimization for arbitrary objective functions presents a dual challenge. First, one needs to repeatedly estimate the objective function; when no closed-form expression is available, this is only possible through simulation. Second, one has to face the possibility of determining local, rather than global, optima. In this paper, we show how the stochastic comparison approach recently proposed in Ref. 1 for discrete optimization can be used in continuous optimization. We prove that the continuous stochastic comparison algorithm converges to an -neighborhood of the global optimum for any >0. Several applications of this approach to problems with different features are provided and compared to simulated annealing and gradient descent algorithms.This work was supported in part by the National Science Foundation under Grants EID-92-12122 and ECS-88-01912, and by a Grant from United Technologies/Otis Elevator Company.     

Conditional stability estimation for an inverse boundary problem with non-smooth boundary in

In this paper, we investigate an inverse problem of determining a shape of a part of the boundary of a bounded domain in by a solution to a Cauchy problem of the Laplace equation. Assuming that the unknown part is a Lipschitz continuous surface, we give a logarithmic conditional stability estimate in determining the part of boundary under reasonably a priori information of an unknown part. The keys are the complex extension and estimates for a harmonic measure.

     

How does a stochastic optimization/approximation algorithm adapt to a randomly evolving optimum/root with jump Markov sample paths

Stochastic optimization/approximation algorithms are widely used to recursively estimate the optimum of a suitable function or its root under noisy observations when this optimum or root is a constant or evolves randomly according to slowly time-varying continuous sample paths. In comparison, this paper analyzes the asymptotic properties of stochastic optimization/approximation algorithms for recursively estimating the optimum or root when it evolves rapidly with nonsmooth (jump-changing) sample paths. The resulting problem falls into the category of regime-switching stochastic approximation algorithms with two-time scales. Motivated by emerging applications in wireless communications, and system identification, we analyze asymptotic behavior of such algorithms. Our analysis assumes that the noisy observations contain a (nonsmooth) jump process modeled by a discrete-time Markov chain whose transition frequency varies much faster than the adaptation rate of the stochastic optimization algorithm. Using stochastic averaging, we prove convergence of the algorithm. Rate of convergence of the algorithm is obtained via bounds on the estimation errors and diffusion approximations. Remarks on improving the convergence rates through iterate averaging, and limit mean dynamics represented by differential inclusions are also presented. The research of G. Yin was supported in part by the National Science Foundation under DMS-0603287, in part by the National Security Agency under MSPF-068-029, and in part by the National Natural Science Foundation of China under #60574069. The research of C. Ion was supported in part by the Wayne State University Rumble Fellowship. The research of V. Krishnamurthy was supported in part by NSERC (Canada).     

Optimal State Estimation in Networked Systems with Asynchronous Communication Channels and Switched Sensors

We study a linear discrete-time partially observed system perturbed by a white noise. The observations are transmitted to the estimator via communication channels with irregular transmission times. Various measurement signals may incur independent delays, arrive at the estimator out of order, and be lost or corrupted. The estimator is given a dynamic control over the sensors by taking part in producing the signals to be sent via the channels. The minimum variance state estimate and the optimal sensor control strategy are obtained. Ideas of model predictive control are applied to derive a nonoptimal but implementable in real time method for sensor control This work was supported by the Australian Research Council.     

The optimum prepaid monetary incentives for mail surveys

Increasing mail-survey response using monetary incentives is a proven, but not always cost-effective method in every population. This paper tackles the questions of whether it is worth using monetary incentives and the size of the inducement by testing a logit model of the impact of prepaid monetary incentives on response rates in consumer and organizational mail surveys. The results support their use and show that the inducement value makes a significant impact on the effect size. Importantly, no significant differences were found between consumer and organizational populations. A cost–benefit model is developed to estimate the optimum incentive when attempting to minimize overall survey costs for a given sample size.     

考虑专家偏好的创新型企业创新能力评价方法

为解决创新型企业创新能力评价过程中专家偏好差异和指标量纲不一致等问题,文章构建了一个科学的评价指标体系,运用考虑专家偏好的效用函数对创新能力评价指标进行效用估算,并计算灰色关联系数;最后运用TOPSIS方法对创新型企业的创新能力进行综合评价和排序。数值实例证明了该方法的科学性和实用性。     

核估计在医疗费用分布研究中的应用

医疗费用的分布是健康保险的精算基础之一.本文研究了某团体一年的住院医疗费用记录,运用非参数模型中的核密度估计方法,对每次住院费用对数的分布密度进行了估计,并用它进一步估计了每次住院费用的均值和方差,取得了较好的效果.在此基础上,本文对拟合结果及其对健康保险的启示作了一些讨论.     

Maximum Kernel Likelihood Estimation

We introduce an estimator for the population mean based on maximizing likelihoods formed by parameterizing a kernel density estimate. Due to these origins, we have dubbed the estimator the maximum kernel likelihood estimate (MKLE). A speedy computational method to compute the MKLE based on binning is implemented in a simulation study which shows that the MKLE at an optimal bandwidth is decidedly superior in terms of efficiency to the sample mean and other measures of location for heavy tailed symmetric distributions. An empirical rule and a computational method to estimate this optimal bandwidth are developed and used to construct bootstrap confidence intervals for the population mean. We show that the intervals have approximately nominal coverage and have significantly smaller average width than the standard t and z intervals. Finally, we develop some mathematical properties for a very close approximation to the MKLE called the kernel mean. In particular, we demonstrate that the kernel mean is indeed unbiased for the population mean for symmetric distributions.