二维隐马氏模型的一种简化算法及其参数估计

针对现有的二维隐马氏模型算法给出了一种简化算法及参数估计方法。该算法与现有的算法相比非常简单。基于此方法给出了相应的识别方法和参数估计,并且该估计与极大似然估计是等同的。     

马氏综合权重距离判别法在中小型滑坡灾害评估中的应用

我国西南地区滑坡灾害具有点多面广、规模小、危害大的特点,群测群防是目前中小型滑坡灾害最为有效的预防手段。针对这种现状,为进一步完善群测群防体系,在马氏距离判别法和加权马氏距离判别法的基础上对权重值进行了修正改进,提出了一种新的判别分析方法——马氏综合权重距离判别法。该方法不仅保留了加权马氏距离判别中区分每一个指标重要性差异的优势,并且结合德尔菲法对权重值进一步修正,弥补了原来加权马氏距离法在判别过程中距离函数出现负值的不足。从本质上来说,这种方法是把由样本驱动的权重值和实际工程经验的权重值相结合,使权重值的取值更加符合实际。以该方法为基础,在一定的历史资料背景下,从滑坡致灾因素和孕灾因素考虑,选取适当的判别因子,建立判别模型,对未知潜在滑坡灾害进行判别归类,并且与马氏距离判别法和Fisher判别法比较。研究发现,马氏综合权重距离判别法具有更高的可靠度和判别精度,适于在群测群防体系中进一步推广使用。     

基于马氏距离的飞行缺失数据估计方法

针对基于欧式距离的最近邻居的缺失值估计算法的不足,提出了一种基于马氏距离的估计算法来估计飞行数据集中的缺失数据.该算法通过飞行数据之间的马氏距离来选择最近邻居数据,并将已得到的估计值应用到后续的估计过程中,然后采用信息熵来计算最近邻居的加权系数,得到缺失数据的估计值.仿真结果表明该算法优于基于欧式距离的最近邻居缺失值处理算法,是一种有效的飞行数据缺失值估计方法.     

基于DCPM-FGA的隐马氏模型的参数训练

建立了一种将直接比较比例方法与模糊遗传算法结合在一起用于隐马氏模型参数训练的方法。针对不同的码本大小进行了仿真试验。结果表明,在码本较大时该方法效果要好于B W算法和Viterbi算法,码本较小时两种方法性能相近。     

马氏链的战术导弹发射能力评估模型

采用马氏链状态转移的方法,对战术导弹发射单元与发射阵地在作战过程中的状态变化进行了研究。在此基础上,对战术导弹在战争条件下的发射能力进行研究,建立评估战术导弹发射能力的动态模型。通过仿真试验,验证了模型的可靠性和有效性。     

舰艇CGF搜潜过程马氏决策规划模型

舰艇搜索潜艇的过程可以近似地看作一种马尔可夫过程.研究了潜艇训练仿真系统中舰艇CGF(计算机生成兵力)搜索潜艇过程中的状态转移过程和随机搜索发现目标的概率模型,将舰艇CGF搜索潜艇的过程分为若干独立的阶段,建立了搜潜过程马氏决策规划模型,提出了在各种不同初始搜索状态下的舰艇搜索策略.仿真结果给出了舰艇CGF的最优搜索策略集合和发现概率,验证了马氏决策规划模型的有效性.     

基于半马氏决策过程的电容器维修策略优化

针对电容器随机劣化失效的特点,采用半马氏决策过程解决其预防性维修方案设计和维修策略优化问题.在电容器随机劣化的Gaussian-Poisson模型基础上,以检测周期为优化变量,同时考虑预防性维修次数阈值的影响,建立电容器长期运行费用率优化模型.分析表明,对该型号电容器来说,预防性维修相比于事后修理更加有利于节省部件长期运行的费用率.     

灰色加权马氏链组合在航空装备事故预测中的应用

为提高传统灰色马氏链组合模型的航空装备事故预测水平,引入加权马氏链,提出了一种改进的灰色马氏链组合预测方法。该方法先通过建立灰色模型,提取事故序列的趋势信息,然后再利用灰色残余信息构建加权马氏链模型,合理发挥各步长马氏链的作用,以期准确刻画随机波动规律。为验证其有效性,在美国空军A级飞行事故万时率实际数据基础上,建立了灰色加权马氏链组合预测模型,结果表明,模型对2000年~2002年预测的相对误差平均控制在4.59%以内,远高于灰色叠加马氏链模型,所建的模型能够比较客观地反映航空安全的未来发展现状。     

基于期望系统噪声的自适应机动目标跟踪研究

状态估计是目标跟踪中的基本问题,也是目标跟踪的一个难点。首先对标准IMM算法的优缺点进行了论述,针对其缺点和不足,提出了基于期望系统噪声模型（MIMM）的自适应多模型算法,该算法能有效地对机动目标的状态进行自适应估计。仿真结果表明,该算法比标准的IMM算法有较好的改善。     

一种基于改进SIFT的航拍图像自动配准算法

针对传统SIFT算法匹配时间长、错匹配较多等问题,提出了一种基于改进SIFT特征的航拍图像自动配准算法.首先,通过特征点检测时设定检测极值点数目,按照DOG空间层次结构由粗到精来搜索特征点,并使用改进的SIFT特征描述符生成算法;其次,利用最近邻匹配准则进行初步匹配得到初始匹配点对,并采用双向匹配方法对匹配特征点对进行筛选;然后,基于马氏距离的特征点相似度量方法进行二次匹配,并使用RANSAC算法求取仿射变换模型;最后,通过双线性插值对变换后的图像进行重采样和插值.实验结果表明:该算法可以实现航拍图像之间的有效配准,在配准性能上优于传统SIFT算法.     

Model parameter estimation and residual life prediction for a partially observable failing system

We consider a partially observable degrading system subject to condition monitoring and random failure. The system's condition is categorized into one of three states: a healthy state, a warning state, and a failure state. Only the failure state is observable. While the system is operational, vector data that is stochastically related to the system state is obtained through condition monitoring at regular sampling epochs. The state process evolution follows a hidden semi‐Markov model (HSMM) and Erlang distribution is used for modeling the system's sojourn time in each of its operational states. The Expectation‐maximization (EM) algorithm is applied to estimate the state and observation parameters of the HSMM. Explicit formulas for several important quantities for the system residual life estimation such as the conditional reliability function and the mean residual life are derived in terms of the posterior probability that the system is in the warning state. Numerical examples are presented to demonstrate the applicability of the estimation procedure and failure prediction method. A comparison results with hidden Markov modeling are provided to illustrate the effectiveness of the proposed model. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 190–205, 2015     

演化硬件中变异概率对进化算法首次命中时间的影响

在(1+1)EAs中,采用马尔可夫链推移时间分析法,推导出了平均首次命中时间的表达式。从理论上分析了变异概率对平均首次命中时间的影响。结果表明适当的变异概率会缩短平均首次命中时间,加快进化算法的寻优时间。     

Computation techniques for large scale undiscounted markov decision processes

In this paper we consider computation techniques associated with the optimization of large scale Markov decision processes. Markov decision processes and the successive approximation procedure of White are described. Then a procedure for scaling continuous time and renewal processes so that they are amenable to the White procedure is discussed. The effect of the scale factor value on the convergence rate of the procedure and insights into proper scale factor selection are given.     

一种面向个性化服务的无需反例集的用户建模方法

随着WWW信息的快速增长 ,查找用户感兴趣的信息变得越来越耗时耗力。个性化服务能为不同的用户提供有针对性的服务 ,日益受到研究者的重视。用户建模是实现个性化服务的关键技术。传统的需要正、反例集作为训练例集的用户建模方法容易干扰用户的正常浏览 ,或者由于推断失误而引入噪声。基于遗传算法和k近邻方法提出了一种无需反例集的用户建模方法 ,该方法被应用于个性化信息过滤中。实验结果表明 ,基于无需反例集的用户建模方法的信息过滤算法可以达到 73 91%的过滤率和 94 4 4 %的过滤精度。无需反例集的用户建模方法是一种可行、高效的用户建模方法     

Comparison of stochastic processes used in sonar detection models

In any model for a sonar detection process, some assumption must be made about the nature of the acoustic fluctuation process. Two processes that are widely used in this role are the jump process and the Gauss-Markov process. These processes are similar in that they are both stationer) Markov processes and have autocovariance functions of the form s?²exp(—γt). For these reasons, it might be believed that one could use either of these processes and get comparable results if all one is interested in is computing cumulative detection probabilities or mean time to gain or lose contact. However, such is not the case in that vastly different results can be obtained in some applications. An application of this sort is presented. We also present necessary and sufficient conditions for a threshold to have the property that it is almost surely crossed by the jump process, or by the Gauss-Markov process. This affords another method of comparison.     

无人机集群作战中连续时间Markov链模型的求解方法

针对无人机集群目标作战解析建模时在状态转移过程中计算速率低的问题,提出了一种基于行压缩存储的四阶Runge-Kutta法。根据无人机集群作战样式将无人机集群作战过程划分为三个阶段,并分阶段对无人机集群作战的状态转移过程建立连续时间Markov链模型。以无人机集群完成作战任务的可靠性作为求解指标,运用四阶Runge-Kutta法对Markov模型进行求解。由于求解过程中速率转移矩阵具有稀疏特性,采用基于行压缩存储的算法优化求解速率。仿真实验表明,运用连续时间Markov理论建立的无人机集群作战过程模型的有效性和可行性优于其他模型。同时,与其他算法及模型相比,该算法计算速率更高、能更好地满足结果精度的可靠性需求,进一步说明了本算法的优越性。 〖BHDWG8,WK10YQ,DK1*2,WK1*2D〗〖XCHSC.TIF;%129%129〗听语音 聊科研与作者互动     

Analysis of a finite‐buffer bulk‐service queue with discrete‐Markovian arrival process: D‐MAP/Ga,b/1/N

Discrete‐time queues with D‐MAP arrival process are more useful in modeling and performance analysis of telecommunication networks based on the ATM environment. This paper analyzes a finite‐buffer discrete‐time queue with general bulk‐service rule, wherein the arrival process is D‐MAP and service times are arbitrarily and independently distributed. The distributions of buffer contents at various epochs (departure, random, and prearrival) have been obtained using imbedded Markov chain and supplementary variable methods. Finally, some performance measures such as loss probability and average delay are discussed. Numerical results are also presented in some cases. © 2003 Wiley Periodicals, Inc. Naval Research Logistics 50: 345–363, 2003.     

Modeling and analysis of a mixed‐model assembly line with stochastic operation times

We consider a mixed‐model assembly line (MMAL) comprised a set of workstations and a conveyor. The workstations are arranged in a serial configuration. The conveyor moves at a constant speed along the workstations. Initial units belonging to different models are successively fed onto the conveyor, and they are moved by the conveyor to pass through the workstations to gradually generate final products. All assembling tasks are manually performed with operation times to be stochastic. An important performance measure of MMALs is overload times that refer to uncompleted operations for operators within their work zones. This paper establishes a method to analyze the expected overload times for MMALs with stochastic operation times. The operation processes of operators form discrete time nonhomogeneous Markov processes with continuous state spaces. For a given daily production schedule, the expected overload times involve in analyzing the Markov processes for finite horizon. Based on some important properties of the performance measure, we propose an efficient approach for calculating the expected overload times. Numerical computations show that the results are very satisfactory. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

基于MUSIC算法的罗兰C接收机天地波自动识别方法研究

针对常规罗兰C接收机天地波识别方法不能有效利用信号能量的缺点,根据天波到来时间的估计与合成信号频率的分离相似的特点,提出了一种基于多信号分类算法估计天波延迟的方法.该方法在低信噪比条件下可分离出地波和天波,并能根据天波的延迟变化实时地选择接收机采样基准点的最佳位置.计算机仿真结果表明,该方法可以用于天地波的自动识别,能增加基准点处的信噪比,因而能显著提高罗兰C接收机的性能.     

效能指数的机载火控雷达研制费用估算模型

为提高机载电子设备研制、生产费用估算的精度,提高估算模型的应用范围和时间跨度,构建了基于效能指数的机载火控雷达研制费用估算模型。在分析机载电子设备研制费用建模特点的基础上,以军用飞机的机载火控雷达设备为研究对象,探讨了其效能指数的构建方法,建立了基于效能指数的研制费用参数模型,并进行实例分析。结果表明,由于效能指数考虑了众多费用驱动因素,该模型形式简明直观,相对误差较小,为研究机载电子设备的费用估算进行了有益的探索。