水的汽液相平衡及径向分布的分子模拟研究

采用J．R．Errington等提出的固定点电荷水分子新模型,分别以Buckingham Exp-5势能结合哥伦布势能模型计算分子间的短程作用及Ewald Summation方法计算电荷间的长程作用,采用恒NVT吉布斯系综Monte Carlo模拟方法模拟了水在300K～600K下的汽液相平衡,并采用经典的恒NVT系综Monte Carlo模拟方法模拟了水在温度T=300K,ρ=1．0g／cm^3下的原子对径向分布函数。模拟结果与文献中的实验值吻合良好。本文工作为推广至更复杂水溶液体系的模拟打下了良好的基础。     

二元自由基共聚合的Monte Carlo模拟

采用Monte Carlo模拟计算方法、可视化编程语言Visual Basic,以研究非稳态、考虑前末端效应、聚合反应可逆等情况下二元自由基共聚合反应,以期得到共聚物的瞬时组成、组成分布、序列分布等决定共聚物效能的重要参数。以数值模拟代替实际实验,不仅可节约成本,而且可减少工作量。模拟结果显示,Monte Carlo模拟值与实验值符合较好,说明本模拟过程模拟效果较好,结果可靠,可用来模拟未知共聚合体系,并为实际共聚物的合成提供帮助。     

基于加性无迹卡尔曼滤波的雷达目标跟踪方法

提出一种基于加性无迹卡尔曼滤波的雷达目标跟踪方法。雷达跟踪系统为离散非线性系统,传统的解决方法是使用扩展卡尔曼滤波。无迹卡尔曼滤波用少量采样点表示随机变量的分布,通过非线性系统传播,能以三阶精度获得非线性变换的均值和方差的估计。用无迹卡尔曼滤波进行雷达目标跟踪。通过Monte Carlo仿真,验证了该滤波算法比传统的扩展卡尔曼滤波具有更高的滤波精度。     

薄层中药物释放的Monte Carlo模拟

描述了一种用于研究药物由搽剂经角质层转运至受体过程的直接Monte Carlo模拟方法。对角质层药物浓度为零和搽剂与角质层药物浓度处于平衡两种初始条件下的药物转运过程进行了模拟。与解析Laplace变换结合数值求逆的结果比较表明,这种Monte Carlo模拟方法准确、可靠、方便,很容易用于更复杂的体系。     

基于Monte Carlo估计的免疫检测器分布优化算法

针对免疫实值检测器的黑洞和边界入侵问题,分析规模对检测性能的影响,提出一种基于Monte Carlo估计的检测器分布优化算法,以Monte Carlo方法估计检测器对非自体空间的覆盖效果作为算法结束的条件,通过优秀子代替代不合时宜的父代来完成检测器的分布优化处理。经实验测试表明,该算法不仅可以有效地降低黑洞,而且能够以更少的检测器更精确地覆盖非自体空间,从而提升检测器的检测性能。     

活性自由基共聚物的微组成与微序列结构的计算机模拟

为了研究活性自由基共聚物的组成、平均段长、链段分布及各元组浓度等重要结构量,本文运用Monte Carlo模拟方法设计一个计算机程序,模拟具有休眠一活化过程的活性自由基共聚过程,并分析所得到的链结构及微结构相关参数,确定了反应参数和聚合物产物结构的关系.     

随机网格回归Monte Carlo双UAVs最优目标协调跟踪

针对多个无人机(unmanned aerial vehicle,UAV)执行基于视觉的目标跟踪的最佳协调问题,提高不可预知的地面目标的最佳结合点的视觉测量效果,提出了一种基于随机网格回归Monte Carlo的UAV最优目标跟踪策略。首先,通过无人机动力学和目标动力学分析,获得双UAV情况下的随机最优协调控制目标;其次,针对提出的控制目标,引入Monte Carlo求解方案,同时为解决标准Monte Carlo方案中存在的状态空间维度较高,计算复杂且精度不高的问题,利用随机网格方式构建回归Monte Carlo方案,实现UAV的最优协调控制;最后,通过仿真实验验证了所提方法的有效性。     

凝并与沉降共存的直接蒙特卡罗模拟及其验证

气溶胶动力学方程是描述气溶胶动力学变化的主要方程,凝并与沉降共存问题是相关领域的常见的问题。建立了对该类问题的分段算法和直接蒙特卡罗模拟(Direct Simulation Monte Carlo method,DSMC)计算方法,对DSMC中事件后系统参数的更新方法进行了改进,并提出了用纳入解析解的分段算法的结果对DSMC模拟结果进行验证的方法。结果表明,改进的DSMC算法显著提高了计算效率,模拟结果与其它算法的结果一致。     

基于序贯无迹卡尔曼滤波的雷达目标跟踪方法

提出一种基于序贯无迹卡尔曼滤波的雷达目标跟踪方法。雷达跟踪系统为离散非线性系统,传统的解决方法是使用扩展卡尔曼滤波。无迹卡尔曼滤波用少量采样点表示随机变量的分布,通过非线性系统传播,能以三阶精度获得非线性变换的均值和方差的估计。为了提高无迹卡尔曼滤波的精度,用序贯无迹卡尔曼滤波方法依次处理方位角、俯仰角和距离,来进行雷达目标跟踪。通过Monte Carlo仿真,验证了该滤波算法比传统的扩展卡尔曼滤波具有更高的滤波精度和更高的计算效率。     

基于特征粒子的Monte Carlo自定位方法

提出了一种基于Monte Carlo方法的多机器人自定位方法．该方法在机器人进行自定位时,对用来估计机器人位置的MCL(Monte Carlo Localization)粒子空间进行栅格划分,然后采用可变栅格法获得能代表所有粒子整体特性的特征粒子集．因为特征粒子的数量较粒子总数大大减少,该方法能避免直接将Monte Carlo方法应用于多机器人定位中产生的维数灾的问题,可以在保证精度的情况下降低运算复杂度．仿真结果表明,该方法能较好地满足多机器人自定位的要求．     

计算多重积分的均匀随机数蒙特卡罗法的实现

蒙特卡罗法是用一系列随机数来近似解决问题的一种方法。采用均匀随机数蒙特卡罗法计算多重积分是一种简单而有效的方法,其程序结构简单,易于编制和调试。本文给出了采用均匀随机数蒙特卡罗法计算多重积分的步骤、算法流程,并给出了实例的具体实现过程。     

Comparison of the approximate solutions of a noisy non-linear system based on Monte Carlo simulation

This paper is concerned with the comparison of the approximate methods in order to evaluate the values of state variables of a noisy nonlinear system by Monte Carlo simulation. The approximate methods considered here are (1) the stochastic linearisation, (2) the first-order approximation and (3) the second-order approximation. It is shown that the order of the accuracies of the covariance based on the approximate equation of Riccati type does not always coincide with the order of the practical error covariance of the original system which is obtained by using Monte Carlo simulation. Therefore the comparison of the accuracies should not be carried out by the solution obtained from the covariance equation but by the practical error covariance of the original system directly. This result is demonstrated with numerical examples. The characteristics of the random numbers which are used for Monte Carlo simulation are examined by various kinds of tests.     

A general discretization procedure for reliability computation in complex stress–strength models

This paper proposes, implements, and evaluates an original discretization method for estimating the reliability of systems for which stress and strength are defined as complex functions of continuous random variables, when reliability is not derivable through common analytic techniques. This method is compared to two other discretization approaches appeared in the literature, and subjected to a comparative closeness study comprising some engineering applications. In this study, both a normal and a non-normal distribution for the random variables involved are analyzed, focusing in the latter case on the Weibull distribution. The results show that the proposal is very effective in terms of the closeness of the estimates to the true (simulated) value of reliability. The method, due to its general applicability, is theoretically suitable for any parametric family and works with a small fraction of the calculation load needed for obtaining the true value by Monte Carlo simulation.     

基于故障树的系统可靠性仿真分析

如何推断系统的寿命分布,是目前可靠性工程领域的一个重要问题.文章应用改进的故障树分析法建立系统的仿真模型,利用蒙特卡洛法在计算机上产生等价于基本部件寿命实现值的随机数,将其代入到系统的仿真模型中,经过逻辑运算得到等价于系统寿命实现值的随机数.这个过程重复多次,便得到足够多的代表系统寿命的实现值的随机序列.在概率图中对随机序列进行统计推断,从而得到系统的寿命分布.此方法简单易行,计算结果精度高,对系统的可靠性分析有重要意义.     

体系对抗仿真中实验次数的自适应控制方法

在方案优化过程中,为消除随机因素的影响,需要进行蒙特卡洛仿真。为此,提出将置信区间引入到体系对抗仿真中,根据仿真过程中产生的数据样本确定蒙特卡洛仿真是否结束。依据数据样本的特性,对置信区间法进行相应的扩展,在获取满足精度需求的解的条件下,尽量缩减仿真实验次数。仿真实验结果表明,扩展置信区间法能够具备对蒙特卡洛仿真的自适应控制能力。     

The estimation of probability distribution of SDE by only one sample trajectory

This paper focuses on the method of the simulation of a stochastic system and the main method of our paper is the Monte Carlo computation simulation method. Taking the stochastic Logistic equation as an example, we present the simulation of the sample trajectory by Euler scheme and the invariant probability distribution of stochastic differential equations with the Monte Carlo method. We also compare the simulation result with the analytical result for the autonomous stochastic Logistic model. Moreover, the stochastic Logistic equation with Markovian switching which is described by a Markov chain taking values in a finite state space is considered.     

毁伤概率评估的加权蒙特卡洛方法

由于经典蒙特卡洛方法的仿真效率不高,文中利用概率论和数理统计的基本原理,可以推导得到加权蒙特卡洛方法.加权蒙特卡罗方法不但能有效的缩小了样本方差,还能提高目标事件出现概率,相当于提高了样本的抽样效率,从而提高了仿真效率.与此同时,文中分别利用经典蒙特卡洛和加权蒙特卡洛这两种方法对典型目标进行了仿真计算,仿真计算最终证明了加权蒙特卡洛方法的仿真效率明显优于经典蒙特卡洛方法,能使仿真工作量成数量级的衰减.     

Testing random number generators for microcomputer applications of Monte Carlo simulation

Monte Carlo simulation is a venerable method of solving probabilistic modeling problems, but has historically been thought of as a mainframe technique because of its computational burden. With the great increases in the speed and power of microcomputers, there is increasing interest in microcomputer-based Monte Carlo techniques applied to probabilistic environmental modeling. Effective Monte Carlo simulation requires fast, accurate random number generators and generator performance is often software and hardware specific. This paper describes a package of diagnostic programs titled RANDALIZE that has been developed to interrogate proposed random number generators to determine if they yield sufficiently random variates with the desired probabilistic properties. The methods used to interrogate a random number sequence are defined and illustrated. Methods for interpreting the results are discussed. Example results are presented to illustrate program functions.