态势估计中一种基于最大后验概率估计的时间推理方法

态势估计中统计时间推理在许多应用中非常重要。Ｋｉｒｉｌｏｖ的基于极大似然估计（ＭａｘｉｍｕｍＬｉｋｅｌｉｈｏｏｄＥｓｔｉｍａｔｉｏｎ，ＭＬＥ）的推理方法将未知时间变量看作常数，忽略了它的先验信息，估计方差较大。针对这一问题，本文首先建立了已知时间信息和未知时间变量之间的关系模型，这一模型可用来解释Ｋｉｒｉｌｏｖ的方法；然后在这一模型下，将未知时间变量扩展为随机变量，采用基于最大后验概率估计（ＭａｘｉｍｕｍａＰｏｓ－ｔｅｒｉｏｒｉＥｓｔｉｍａｔｉｏｎ，ＭＡＰ）的方法进行统计时间推理。对两种推理算法的性能进行了分析和比较，发现在较宽的范围内，基于ＭＡＰ的方法性能优于基于ＭＬＥ的方法。     

噪声统计的Bayes 极大验后估计与系统自适滤波

对于具有测量的线性离散系统,在系统动力学噪声与测量统计特性未知的情况下,本文推导出了噪声统计特性在具有正态－逆Ｗｉｓｈａｒｔ分布验前信息情况之下的Ｂａｙｅｓ极大验后（ＭＡＰ）估计及其实时迫近,从而获得了系统的自适应滤波     

MEASUREMENT OF PLASMA POTENTIAL USING EMISSIVE PROBE IN A MAGNETIC MIRROR

ＭＥＡＳＵＲＥＭＥＮＴＯＦＰＬＡＳＭＡＰＯＴＥＮＴＩＡＬＵＳＩＮＧＥＭＩＳＳＩＶＥＰＲＯＢＥＩＮＡＭＡＧＮＥＴＩＣＭＩＲＲＯＲＺｈａｎｇＪｉａｎｄｅ（ＤｅｐａｒｔｍｅｎｔｏｆＡｐｐｌｉｅｄＰｈｙｓｉｃｓ,ＮＵＤＴ,Ｃｈａｎｇｓｈａ,４１００７３）Ｆａ...     

指数分布下可靠性参数的样条经验 Bayes 估计

基于可靠性参数的验前密度函数的样条密度估计，本文推导出指数分布失效率和可靠性函数的经验Ｂａｙｅｓ估计，并采用数学仿真将其与传统的Ｂａｙｅｓ方法，如Ｇａｍ ｍ ａ 验前分布的情况，进行了比较。仿真结果表明，本文的方法是有效的     

(G2-continuous) SHAPE PRESERVING CUBIC INTERPOLATION SPLINE CURVE

（Ｇ~２－ｃｏｎｔｉｎｕｏｕｓ）ＳＨＡＰＥＰＲＥＳＥＲＶＩＮＧＣＵＢＩＣＩＮＴＥＲＰＯＬＡＴＩＯＮＳＰＬＩＮＥ　ＣＵＲＶＥＦａｎｇＫｕｉ（ＤｅｐａｒｔｍｅｎｔｏｆＳｙｓｔｅｍＥｎｇｉｎｅｅｒｉｎｇａｎｄＭａｔｈｅｍａｔｉｃｓ,ＮＵＤＴ,Ｃｈａｎｇｓｈ?..     

基于稀疏贝叶斯学习的DOA估计

针对基于l1范数约束的稀疏表示DOA(Direction Of Arrival)估计算法对初始参数较为敏感的问题,提出了一种基于稀疏贝叶斯学习的DOA估计算法。首先通过信号来波方向的空间采样构造冗余字典,将阵列信号处理中的DOA估计信号模型转化为压缩感知中的稀疏重构信号模型。然后基于经验贝叶斯推理的方法,将待估计的稀疏系数值用方差未知的联合高斯分布描述,而未知的方差值决定了待估计系数的稀疏性。通过观测数据估计得到未知的方差,进而得到信号的DOA估计值。仿真结果表明,提出的算法有较高估计精度,并且对非相干信源和相干信源都具有较好的估计性能。     

A BENDING DAMAGE THEORY FOR BEAMS UNDER PURE BENDING LOADS

ＡＢＥＮＤＩＮＧＤＡＭＡＧＥＴＨＥＯＲＹＦＯＲＢＥＡＭＳＵＮＤＥＲＰＵＲＥＢＥＮＤＩＮＧＬＯＡＤＳＹａｎｇＧｕａｎｇｓｏｎｇ;ＬｕＹｉｎｃｈｕ;ＪｉｎＸｉｎ（ＤｅｐａｒｔｍｅｎｔｏｆＡｅｒｏｓｐａｃｅＴｅｃｈｎｏｌｏｇｙ,ＮＵＤＴ,Ｃｈａｎｇｓｈａ,...     

基于贝叶斯网络的态势估计时间推理方法

基于贝叶斯网络的态势估计方法是目前态势估计领域中的主要方法之一,然而,传统的贝叶斯网络不具备时间语义,因此无法解决态势估计中的时间推理问题.基于此,对贝叶斯网络进行改造,研究了时间贝叶斯网络的构建方法.通过一个战场想定,说明了时间贝叶斯网络构建、推理的过程与方法,证明了提出方法的有效性.     

CALCULATION OF BURNING RATE CHARACTERISTICS IN ACCELERATED FIELD FOR SOME ALUMINIZED SOLID PROPELLANTS

ＣＡＬＣＵＬＡＴＩＯＮＯＦＢＵＲＮＩＮＧＲＡＴＥＣＨＡＲＡＣＴＥＲＩＳＴＩＣＳＩＮＡＣＣＥＬＥＲＡＴＥＤＦＩＥＬＤＦＯＲＳＯＭＥＡＬＵＭＩＮＩＺＥＤＳＯＬＩＤＰＲＯＰＥＬＬＡＮＴＳＣａｏＴａｉｙｕｅ（ＤｅｐａｒｔｍｅｎｔｏｆＡｅｒｏｓｐａｃｅＴｅｃｈ...     

基于Mean-Shift的传感器动态偏差估计方法

针对系统误差变化规律未知时的传感器探测系统偏差估计问题,提出了一种改进的基于Mean-Shift(均值偏移)的传感器动态偏差估计算法.该算法利用Mean-Shift方法对不同样本点对估计结果贡献不同的特点,根据样本点偏离均值的偏移量以及偏移时间构建权系数.仿真结果表明,该方法在系统误差变化规律未知的情况下,可有效估计多...     

An optimal ordering policy for situations with uncertainty in supply

A one-period inventory model where supply is a random variable with mean proportional to the quantity ordered has been considered. Under new better than used in expectation assumption on the supply variable, a strategy which maximizes a minimum profit has been suggested. An estimate for this maximin order quantity whenever the (customer) demand distribution is unknown has been proposed and almost sure convergence of this estimate to its true value with increasing sample size has been established.     

基于直觉模糊时间Petri网的不确定性时间推理方法

针对Petri网模型在对复杂不确定性时间信息描述和推理方面的局限性,在定义直觉模糊时间函数以及网络变迁约减规则的基础上,融合直觉模糊时序逻辑(IFTL)、直觉模糊Petri网(IFPN)以及线性逻辑推理的理论优势,构建了直觉模糊时间Petri网(IFTPN)推理模型,并提出了基于IFTPN的不确定性时间推理算法,较好地解决了态势评估中冲突事件间的不确定性时间推理问题。最后,通过典型的战场想定验证了该时间推理方法的有效性和优越性。     

近似推理在水雷参数分析专家系统中的应用

为了尽快分析出未知水雷障碍参数,根据水雷战的特点,提出了建立未知水雷障碍参数分析专家系统的观点,对专家系统的设计方法进行了一定的探讨,并针对专家系统建立中的"瓶颈"问题,提出了基于Vague集插值近似推理的专家系统知识自动获取方法,在介绍推理过程的基础上给出了算例.从推理的结果来看,该方法具有较高的可信度,从而为专家系统的研制提供了一定的方法支持.     

态势估计中基于假设检验的统计时间推理方法

在战场态势估计中，常常需要判断事件发生的时间顺序，而对事件发生时间的观测往往含有统计不确定性。这里将假设检验理论引入时间推理，建立了一种判别两个事件发生顺序的新方法，同时研究了在统计不确定性下时间顺序的传递性，从而有效增强了态势估计中对时间关系的描述和推理能力     

On estimating the reliability of a component subject to several different stresses (strengths)

This paper is concerned with estimating p = P(X₁ < Y …, X_n < Y) or q =P (X < Y₁, …, X < Y_n) where the X's and Y's are all independent random variables. Applications to estimation of the reliability p from stress-strength relationships are considered where a component is subject to several stresses X₁, X₂, …, X_N whereas its strength, Y, is a single random variable. Similarly, the reliability q is of interest where a component is made of several parts all with their individual strengths Y₁, Y₂ …, Y_N and a single stress X is applied to the component. When the X's and Y's are independent and normal, maximum likelihood estimates of p and q have been obtained. For the case N = 2 and in some special cases, minimum variance unbiased estimates have been given. When the Y's are all exponential and the X is normal with known variance, but unknown mean (or uniform between 0 and θ, θ being unknown) the minimum variance unbiased estimate of q is established in this paper.     

Adaptive importance sampling for extreme quantile estimation with stochastic black box computer models

Quantile is an important quantity in reliability analysis, as it is related to the resistance level for defining failure events. This study develops a computationally efficient sampling method for estimating extreme quantiles using stochastic black box computer models. Importance sampling has been widely employed as a powerful variance reduction technique to reduce estimation uncertainty and improve computational efficiency in many reliability studies. However, when applied to quantile estimation, importance sampling faces challenges, because a good choice of the importance sampling density relies on information about the unknown quantile. We propose an adaptive method that refines the importance sampling density parameter toward the unknown target quantile value along the iterations. The proposed adaptive scheme allows us to use the simulation outcomes obtained in previous iterations for steering the simulation process to focus on important input areas. We prove some convergence properties of the proposed method and show that our approach can achieve variance reduction over crude Monte Carlo sampling. We demonstrate its estimation efficiency through numerical examples and wind turbine case study.     

An application of servomechanisms to inventory

In recent years, some attention has been devoted to the application of techniques of control theory to inventory management. In particular, H. Vassian (1955) developed a model for a periodic review inventory system utilizing techniques of discrete variable servomechanisms to analyze the system in a cost-free structure. The resulting model is inherently deterministic, however, and emphasizes the control of inventory fluctuation about a safety level by selecting an appropriate order policy. Such an order policy is defined only up to an arbitrary method of forecasting customer demands. The present paper is a continuation of the model developed by Vassian in which exponential smoothing is used as a specific forecasting technique. Full recognition of the probabilistic nature of demand is taken into account and the requirement of minimizing expected inventory level is imposed. In addition, explicit formulas for the variance in inventory are derived as functions of the smoothing constant and the tradeoff between small variance and rapid system response is noted. Finally, in an attempt to remove the bias inherent in exponential smoothing, a modification of that technique is defined and discussed as an alternate forecasting method.     

Waypoint analysis for command and control

Command and Control (C2) in a military setting can be epitomized in battles‐of‐old when commanders would seek high ground to gain superior spatial‐temporal information; from this vantage point, decisions were made and relayed to units in the field. Although the fundamentals remain, technology has changed the practice of C2; for example, enemy units may be observed remotely, with instruments of varying positional accuracy. A basic problem in C2 is the ability to track an enemy object in the battlespace and to forecast its future position; the (extended) Kalman filter provides a straightforward solution. The problem changes fundamentally if one assumes that the moving object is headed for an (unknown) location, or waypoint. This article is concerned with the new problem of estimation of such a waypoint, for which we use Bayesian statistical prediction. The computational burden is greater than an ad hoc regression‐based estimate, which we also develop, but the Bayesian approach has a big advantage in that it yields both a predictor and a measure of its variability. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004     

模糊离散动态贝叶斯网络的目标威胁等级评估

动态贝叶斯网络作为一种智能推理工具在处理不确定推理问题中显示出强大的生命力,但是存在难于处理连续变量的推理问题。将模糊理论与动态贝叶斯网络相结合,提出一种模糊分类的方法,将连续变量模糊分类为动态贝叶斯网络能够应用的证据信息用于推理,并建立目标威胁等级评估模型,应用直接推理算法对该网络进行推理。仿真结果表明,该分类方法与动态贝叶斯网络结合能够很好地处理连续变量推理的问题。