新高阶多变量马尔可夫模型（英文）

本文提出了一种新高阶多变量马尔可夫模型,并对其收敛性进行了分析.给出了模型的参数估计方法.数值实验表明在预测精度方面新高阶多变量马尔可夫模型比高阶多变量马尔可夫模型更加有效.     

马尔可夫链模型在软件可靠性测试中的应用

现行的软件可靠性分析方法禁锢了人们从更简单层次对软件系统的可靠性进行研究。基于上述认识,为了缩短软件测试周期,文中提出了一种新的软件可靠性测试分析方法。该方法把马尔可夫链模型运用于软件可靠性测试中,在测试过程中使用了新的评判准则分析测试结果,通过实例证明了该评判准则的实用性和有效性。从而为更快速地评估软件的可靠性提供了可能。     

多变量时变系统的CARMA模型近似解耦法

通过在线递推辨识出一阶CARMA模型,构造近似的前馈解耦补偿器,进行动态解耦,使之具有自适应动态解耦的效果.     

基于马尔可夫链模型的B&L VP 控制图统计性能分析

在已有VP 控制图基础上,增加了B&L转换规则,构建了基于马尔可夫链模型的B&L VP 控制图统计性能分析模型,给出了统计性能指标ATS、AATS以及ANSW的表达式。针对2组设计参数,将构建的模型与现有VP 控制图进行了比较分析,结果表明当平均运行时间相同时,本文构建的模型在缩短报警时间和减少转换次数方面,都有着明显的优势,而且随着转换参数L的增大,这种优势会增大。     

基于隐马尔可夫模型与神经网络混合的被动声目标分类技术

提出了一种改进型隐马尔可夫模型／神经网络混合分类器,该分类器将隐马尔可夫模型的时间校正能力与神经网络的静态区分能力结合在一起。它首先利用循环无跳转ＨＭＭ模型对第一测试特征序列进行全状态分割。将Ｔ帧特征序列按时间演化顺序校正成Ｎ帧平均状态序列。然后 交其作为ＲＢＦ网络的输入矢量进行分类。实验结果表明,该分类器比单纯的神经网络或隐马尔可夫模型分类器具有更限的分类效果。     

多变量问题等值线形成的数学方法及物理意义

本文应用广义逆解法,将多变量散乱数据曲面(超曲面)拟合,把拟合后的曲面(超曲面)作为多变量问题等值线形成的数学模型,从而把超曲面、等值面、等值线这些几何概念,与等值线形成的物理意义结合起来,使人们能够从离散数据中揭示出所蕴含的物理规律。     

双线性时间序列模型高阶矩的渐近性质

讨论了一般双线性时间序列模型BL（R，r,Q,q）的高阶（2u阶，u=1，2，…）平稳解存在的充分条件，并证明了高阶矩估计量的相和性和渐近正态性。     

一种基于隐马尔可夫模型的多目标方位自动跟踪方法

方位-幅度-时间图(方位瀑布图)是声呐检测设备一种基本的显示图像。方位瀑布图由于在视觉上起着时空积分作用,可提供的信息量很大,声呐兵可通过观察长时间的目标方位轨迹来检测弱目标。本文在这种思想下应用隐马尔可夫模型和Viterbi算法,提出了一种自动提取方位轨迹的方法。计算机仿真研究表明这种方法是简单可行的。     

基于马尔可夫链模型的B&L VP (X)控制图统计性能分析

在已有VP(X)控制图基础上,增加了B&L转换规则,构建了基于马尔可夫链模型的B&L VP(X)控制图统计性能分析模型,给出了统计性能指标ATS、AATS以及ANSW的表达式.针对2组设计参数,将构建的模型与现有VP(X)控制图进行了比较分析,结果表明当平均运行时间相同时,本文构建的模型在缩短报警时间和减少转换次数方面,都有着明显的优势,而且随着转换参数L的增大,这种优势会增大.     

可信服务组合的马尔可夫链模型及其应用

基于马尔可夫链建立了服务组合的形式化模型--马尔可夫链模型,此模型可以在已知组合服务定义规格的情况下由组合服务的定义规格转换得到,也可以在未知组合服务定义规格的情况下由组合服务的执行历史信息得到.不同于多数服务组合形式化模型仅有用于验证组合服务正确性的单一功能,组合服务的马尔可夫链模型具有多种用途,例如可以用来评估组合...     

灰色马尔可夫链在道路交通事故预测中的应用

将灰色系统理论与马尔可夫链相结合,建立道路交通事故灰色马尔可夫链预测模型研究证明．这种模型的预测精度高于GM（1,1）模型,可用于道路交通事故预测。     

On Perturbation Bounds for the Joint Stationary Distribution of Multivariate Markov Chain Models

Multivariate Markov chain models have previously been proposed in for studying dependent multiple categorical data sequences. For a given multivariate Markov chain model, an important problem is to study its joint stationary distribution. In this paper, we use two techniques to present some perturbation bounds for the joint stationary distribution vector of a multivariate Markov chain with s categorical sequences. Numerical examples demonstrate the stability of the model and the effectiveness of our perturbation bounds.     

基于Markov链的供应链产品市场预测分析

供应链的组建类型取决于市场需求的变化趋势,在对Markov链的基本思想进行分析的基础上,利用其平稳性和无后效性的基本特征,构建了基于Markov链的市场随机需求预测模型,提出了模型的相关步骤,并指出构建转移矩阵时应注意的问题.实例证明基于Markov链的产品市场预测模型能有效地预测产品未来市场变化趋势.     

A multi-state Markov model for a short-term reliability analysis of a power generating unit

This paper presents a multi-state Markov model for a coal power generating unit. The paper proposes a technique for the estimation of transition intensities (rates) between the various generating capacity levels of the unit based on field observation. The technique can be applied to such units where output generating capacity is uniformly distributed. In order to estimate the transition intensities a special Markov chain embedded in the observed capacity process was defined. By using this technique, all transition intensities can be estimated from the observed realization of the unit generating capacity stochastic process. The proposed multi-state Markov model was used to calculate important reliability indices such as the Forced Outage Rate (FOR), the Expected Energy Not Supplied (EENS) to consumers, etc. These indices were found for short-time periods (about 100 h). It was shown that these indices are sensibly different from those calculated for a long-term range. Such Markov models could be very useful for power system security analysis and short-term operating decisions.     

Computing Transition Probability in Markov Chain for Early Prediction of Software Reliability

Early prediction of software reliability provides basis for evaluating potential reliability during early stages of a project. It also assists in evaluating the feasibility of proposed reliability requirements and provides a rational basis for design and allocation decisions. Many researchers have proposed different approaches to predict the software reliability based on a Markov model. The transition probabilities in between the states of the Markov model are input parameters to predict the software reliability. In the existing approaches, these probabilities are either assumed on some knowledge or computed using analytical method, and hence, it does not give accurate predicted reliability figure. Some authors compute them using operational profile data, but that is possible only after the deployment of the software, and this is not early prediction. The work in this paper is devoted to demonstrate the computation of transition probability in the Markov reliability model taking a case study. The proposed approach has been validated on 47 sets of real data. Copyright © 2015 John Wiley & Sons, Ltd.     

关于树指标隐Markov链及其等价定义

本文参照直线上隐Markov模型的概念,给出有限树指标隐Markov链的定义.在该定义中,树指标隐Markov链由两个树指标随机过程组成,其中第一个树指标随机过程是树指标Markov链,是不能被直接观测到的隐藏链;第二个树指标随机过程是可被观测的且关于第一个树指标随机过程条件独立,对于树上的任意一个顶点,第二个随机过程此处的取值只信赖于隐藏链中此处的取值.最后,我们给出了树指标隐Markov链的三个等价定义.     

Bayesian Detection of Changepoints in Finite-State Markov Chains for Multiple Sequences

We consider the analysis of sets of categorical sequences consisting of piecewise homogenous Markov segments. The sequences are assumed to be governed by a common underlying process with segments occurring in the same order for each sequence. Segments are defined by a set of unobserved changepoints where the positions and number of changepoints can vary from sequence to sequence. We propose a Bayesian framework for analyzing such data, placing priors on the locations of the changepoints and on the transition matrices and using Markov chain Monte Carlo (MCMC) techniques to obtain posterior samples given the data. Experimental results using simulated data illustrate how the methodology can be used for inference of posterior distributions for parameters and changepoints, as well as the ability to handle considerable variability in the locations of the changepoints across different sequences. We also investigate the application of the approach to sequential data from an application involving monsoonal rainfall patterns. Supplementary materials for this article are available online.     

An Efficient Approximate Markov Chain Method in Dynamic Fault Tree Analysis

Approximate Markov chain method for dynamic fault tree analysis is suggested for both reparable and non‐reparable systems. The approximation is based on truncation, aggregation and elimination of Markov chain states during the process of dynamic fault tree transformation to corresponding Markov chain. The method is valid for small probabilities. For reparable systems, it is true if mean time to repair is much less than mean time to failure. Several examples are studied. Additional simplification is considered in case the system is in a steady state. Copyright © 2015 John Wiley & Sons, Ltd.     

New Approaches in Monitoring Multivariate Categorical Processes based on Contingency Tables in Phase II

In some statistical process control (SPC) applications, quality of a process or product is characterized by contingency table. Contingency tables describe the relation between two or more categorical quality characteristics. In this paper, two new control charts based on the WALD and Stuart score test statistics are designed for monitoring of contingency table‐based processes in Phase‐II. The performances of the proposed control charts are compared with the generalized linear test (GLT) control chart proposed in the literature. The results show the better performance of the proposed control charts under small and moderate shifts. Moreover, new schemes are proposed to diagnose which cell corresponding to different levels of categorical variables is responsible for out‐of‐control signal. In addition, we propose EWMA–WALD and EWMA–Stuart score test control charts to improve the performance of Shewhart‐based control charts in detecting small and moderate shifts in contingency table parameters. Meanwhile, we compare the performances of two proposed EWMA‐based control charts with the ones of three existing control charts called EWMA–GLT, EWMA–GLRT and an EWMA‐type control chart for multivariate binomial/multinomial processes along with the ones of the corresponding Shewhart‐based control charts. A numerical example is given to show the efficiency of the proposed methods. Finally, the effect of parameter estimation in Phase I based on m historical contingency table on the performance of the Shewhart‐based control charts is studied. Copyright © 2016 John Wiley & Sons, Ltd.