确定地下水污染强度的反问题方法

探讨了山东省淄博市张店区沣水南部区域地下水的硫酸污染问题 .根据观测井点的浓度数据 ,将硫酸污染强度的识别问题作为一个已知终值数据的源项反问题 .应用积分恒等式方法分析了未知源函数与已知数据间的数据相容性 ,进而基于最优化策略进行了数据反演 ,计算结果与实际估计值基本吻合     

更新过程理论在交通流上的一个应用

一个更新过程的两个随机变量的分布:间隔分布、计数分布是1—1对应的,但由间隔分布求对应的计数分布的问题尚未很好地解决。在道路断面观测交通流可得到一更新过程,车头时距和车辆到达分别是其间隔和计数。时距分布容易观测得到,而到达分布的观测却较难。因此上述数学问题的解决对交通流理论是非常有意义的,本文将研究之。     

二维热传导方程源强识别反问题的数值求解

本文研究了一类含有时间变量热源的二维热传导方程.利用有限元方法给出了数值求解过程,并在已知热源位置的前提下,根据某点的温度观测值,利用插值方法,将源强识别问题转化为参数反演问题,通过微分进化方法结合最佳摄动量法对源强识别反问题进行了数值模拟,结果表明所提出的方法是可行有效的.     

基于增广的广义卡尔曼滤波模型的高精度参数估计

针对捕食模型、利用已知观测数据、在非线性最小二乘准则下,建立了基于增广的广义卡尔曼滤波模型来解决噪声背景下的高精度参数估计问题,并予以了验证.提出以相平面方程为约束的初值搜索算法,利用Matlab优化工具箱、深度搜索和剪枝加速等技术来提高搜索速度.还建立了二重规划模型以解决观测时间有误差时的高精度估计问题,并对时间误差作了正态分布检验.     

在不确定观测下离散状态时滞系统的最优滤波

研究了在不确定观测下离散状态时滞系统的最优滤波问题,观测值的不确定性则通过一个满足Bernoulli分布且统计特性已知的随机变量来描述. 一般采用状态增广方法将时滞系统转换为无时滞随机系统, 再利用Kalman滤波器的设计方法解决最优状态估计问题, 但是当系统时滞较大时,转换后的系统状态维数很高, 这样增加了计算负担. 为此,基于最小方差估计准则, 利用射影性质和递归射影公式得到了一个新的滤波器设计方法, 而且保证了滤波器的维数与原系统相同.最后, 给出一个仿真例子说明所提方法的有效性.     

随机回归系数和参数的线性估计的可容许性的几个结果

考虑线性模型Y=Xβ+ε(1.1)其中 X 为已知的 n×p 矩阵,n≥p;β和ε分别为不可观测的 p 维和 n 维随机向量;Y是可观测的;Eβ=Aα,Eε=0,COV(?)=V,V 和 A 都已知,α∈R~k 为参数.这     

挖掘函数问题的失分根源

笔者在教学中发现同学们在函数问题的处理中失分现象较为普遍,错误原因又不易察觉,下面对问题中造成错误的根源举例剖析. 一、错用已知条件 数学题中的条件非常严密,解题过程中一定要认真地分析题目中的已知条件,只有正确理解已知条件,才可能将题目正确的做出来.     

基于EM算法的因子分析中隐变量的条件概率密度函数

因子分析是一种重要的多元统计分析技术,可以采用EM算法迭代得到模型的未知参数,其中一个关键的问题就是在已知观测数据和前一次迭代得到的参数估计值的条件下,如何得到隐变量的条件概率密度函数.国内外的有关文献都不加说明地直接给出了这个函数,本文给出了详细的推导过程.     

有重复观测的变系数EV模型的参数估计

构造了有重复观测的变系数EV模型中的诸多参数估计,包括系数函数、测量误差方差以及测量误差与回归误差的协方差等估计,去除了有关测量误差方差已知或可靠比已知的假定.在一些较弱的条件下,证明了所有的这些估计都是强相合的,同时获得了系数函数估计的渐近正态性以及收敛速度.     

生态系统的参数确定问题

通过不同观测数据研究捕食者—被捕食者生态系统参数确定问题.研究了四种情形1.观察数据无误差,并已知一个参数值.这种情况下,参数可由其相轨线和最小二乘法精确确定.2.观察数据无误差,但所有参数未知.此时仅靠相轨线的研究,无论给出多少组精确数据,都无法精确确定这些参数.通过原非线性模型的数值计算和网格搜索法,至少需要4组数据,同样得到了精度较高的参数值.3.当观测数据有误差时,根据解的周期性,引入标准周期的概念,在一个标准周期里讨论参数的确定问题,并利用标准周期内的捕食者与被捕食者的数量均值与系统的平衡点的关系对参数进行修正,然后使用网格法进行搜索,进一步提高了参数的精度.4.当观测时间也有误差时,先选取相对最优的随机正态数对观测时刻进行修正,然后再利用3.的方法估计参数.     

Admissible control and observation operators for Volterra integral equations

The admissibility of observation operators and control operators for linear Volterra systems is studied by means of the theory of composition operators on Hardy spaces.Under certain assumptions it is shown to be equivalent to admissibility for the classical Cauchy problem. A duality between control and observation operators is also established,extending known results for the Cauchy problem,which is a special case.     

Constructing an asymptotic solution of optimal observation problem for a quasilinear differential-algebraic system

The known results on the problem of optimal observation for ordinary quasilinear systems are generalized to differential-algebraic systems.     

Parameterized complexity of the weighted independent set problem beyond graphs of bounded clique number

The maximum independent set problem is known to be NP-hard for graphs in general, but is solvable in polynomial time for graphs in many special classes. It is also known that the problem is generally intractable from a parameterized point of view. A simple Ramsey argument implies the fixed-parameter tractability of the maximum independent set problem in classes of graphs of bounded clique number. Beyond this observation very little is known about the parameterized complexity of the problem in restricted graph families. In the present paper we develop fpt-algorithms for graphs in some classes extending graphs of bounded clique number.     

Selection of certain dichotomous experiments

Summary The problem of allocating a single observation to one of the two available populations is considered. Suppose that a certain characteristic has densityf in one population, and has densityg in the other. On the basis of the value observed, one must specify which population has densityf. It is assumed that when a wrong population is chosen, a certain known loss is incurred. The problem is to allocate the observation so as to minimize the expected loss. General conditions onf andg are derived to decide which population should be selected for taking the observation. Research was supported in part by the National Science Foundation under grant No. SOC79-06386. Work done while on sabbatical leave at Carnegie-Mellon University.     

The Newton-Kleinman method for the optimal control of stable weakly regular linear systems

We give an algorithm to find the approximate solution of the linear-quadratic optimal control problem for stable weakly regular linear systems. This algorithm can be understood as a generalization of the Newton-Kleinman method known from the finite-dimensional theory. The central characteristic of our approach is the possibility to solve problems with unbounded control and observation operators, which is motivated by partial differential equations with boundary control and observation. (© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

等式约束不定最小二乘问题的双曲MGS消去算法

众所周知,加权法是解等式约束不定最小二乘问题的方法之一.通过探讨极限意义下,双曲MGS算法解对应加权问题的本质,得到一类消去算法.实验表明,该算法以和文献中现有的GHQR算法达到一样的精度,但实际计算量只需要GHQR算法的一半.     

Zero-Sum Stochastic Games with Partial Information

We study a zero-sum stochastic game on a Borel state space where the state of the game is not known to the players. Both players take their decisions based on an observation process. We transform this into an equivalent problem with complete information. Then, we establish the existence of a value and optimal strategies for both players.     

Most probable explanations in Bayesian networks: Complexity and tractability

One of the key computational problems in Bayesian networks is computing the maximal posterior probability of a set of variables in the network, given an observation of the values of another set of variables. In its most simple form, this problem is known as the MPE-problem. In this paper, we give an overview of the computational complexity of many problem variants, including enumeration variants, parameterized problems, and approximation strategies to the MPE-problem with and without additional (neither observed nor explained) variables. Many of these complexity results appear elsewhere in the literature; other results have not been published yet. The paper aims to provide a fairly exhaustive overview of both the known and new results.     

Sampling Bias Correction in the Model of Mixtures with Varying Concentrations

Model of mixture with varying concentrations is a generalization of the classical finite mixture model in which the mixing probabilities (concentrations) vary from observation to observation. We consider the case when the concentrations of the mixture components are known, but no assumptions on the distributions of the observed variable are made. The problem is to estimate the moments of the components’ distributions. We propose a modification of the Horvitz-Thompson weighting for moments estimation by observations from mixture with varying concentrations in presence of sampling bias. Consistency of obtained estimators is demonstrated. Results of simulations are presented.