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.     

基于马尔可夫分析的人才拥有量研究

基于马尔可夫分析理论，给出了人才拥有量预测的马尔可夫模型，并且讨论了模型中转移概率的确定及模型的预测质量等问题。     

联合监控均值和方差的累积和控制图优化设计

为了提高监测均值和方差微小偏移的敏感度,围绕生产过程质量控制,建立同时监控均值和方差的累积和控制图.模型考虑均值和方差的变化,针对生产过程中的微小偏差,提出了一个新的累积和控制图,并给出了基于马尔可夫链理论的新控制图的平均链长计算方法.编程求解后对比文献中各控制图的平均链长数据以及更换变量数值改进控制图,通过计算变动比率得出新控制图的检测力度在不同偏移力度下都明显优于其他控制图方法.     

一种机动目标位置自适应估计模型

目标运动模型是机动目标跟踪的基本要素之一。现有的机动目标运动模型有的无法自适应目标运动模式,有的模型复杂,计算量较大。根据牛顿运动定律、声纳目标加速度短时间内一般不会剧烈变化的特性和高阶马尔可夫过程,推导出了适应于加速度渐变、不变等多种机动形式的声纳机动目标位置自适应估计模型。该模型采用单一的状态变量,不存在状态耦合,因此模型简单,计算实时性强。通过对不同运动模式的目标数据进行仿真估计,证明了此估计模型的正确性和自适应性。     

区域物流规模的改进灰色马尔可夫预测

在分析灰色GM(1,1)方法、灰色马尔可夫方法的基础上,建立区域物流规模预测的改进灰色马尔可夫模型,以国家统计局公布的陕西省1997-2006年货物周转量的统计数据为依据,对其2007-2008年的物流规模进行预测.与灰色GM(1,1)方法、灰色马尔可夫方法的预测结果相比,改进灰色马尔可夫链模型对区域物流规模的预测更加有效.     

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...     

主动悬架的一种基于状态反馈的单神经元多变量控制策略

为了以一种简捷的方式来有效控制主动悬架这类典型多变量系统的多输出量,提出了一种基于状态反馈的新型神经元控制策略:采用了单神经元模型,并引入线性二次型最优控制(LQR)中的状态反馈思想和控制律形式.将其应用到1/4主动悬架的控制设计中,研究了悬架参数变化时的鲁棒性能,并和被动悬架及LQR控制进行了性能对比.仿真结果表明,所建议的方法可有效地改进车辆性能,优于LQR控制.而且,对其它类似的LQR多变量控制问题和被控对象,提供了一种可能更有效的新的简便途径.     

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.     

采用自适应邻域马尔可夫场的极化SAR图像分割

提出了一种新的马尔可夫场(MRF)极化SAR图像分割方法,对于图像中的每个像素,利用极化SAR数据总功率(span)图,得到该像素的马尔可夫场自适应邻域系统,且对区域标号过程的参数进行修正.使用该方法对机载极化SAR数据进行分割,实验结果表明,与传统的马尔可夫场方法、最大似然方法相比,该方法可以得到较好的分割结果.     

基于马尔可夫过程的水下运动目标搜索概率

以水下目标搜索为背景,建立了水下目标运动模型、搜索器搜索过程模型、马尔可夫运动目标搜索模型,并以此为基础,仿真分析了水下目标运动服从马尔可夫运动过程条件下,搜索器以5种不同方式对水下目标进行搜索时搜索器的运动路径、对水下目标的搜索概率,为研究水下目标的优化搜索奠定了基础.     

A variable parameters multivariate control chart for simultaneous monitoring of the process mean and variability with measurement errors

Evaluating the effect of measurement errors on either adaptive or simultaneous control charts has been a topic of interest for the researchers in the recent years. Nevertheless, the effect of measurement errors on both adaptive and simultaneous monitoring control charts has not been considered yet. In this paper, through extensive numerical studies, we evaluate the effect of measurement errors on an adaptive (variable parameters) simultaneous multivariate control chart for the mean vector and the variance-covariance matrix of p quality characteristics assumed to follow a multivariate normal distribution. In order to do so, (a) we use eight performance measures computed using a Markov chain model, (b) we consider the effects of multiple measurements as well as the error model's parameters, and (c) we also consider the overall performance of this adaptive simultaneous chart including the chart parameters values optimization, which have never been considered so far for this scheme. At last, a real case is presented in order to illustrate the proposed scheme.     

Monitoring multivariate coefficient of variation with upward Shewhart and EWMA charts in the presence of measurement errors using the linear covariate error model

In practice, measurement errors exist and ignoring their presence may lead to erroneous conclusions in the actual performance of control charts. The implementation of the existing multivariate coefficient of variation (MCV) charts ignores the presence of measurement errors. To address this concern, the performances of the upward Shewhart-MCV and exponentially weighted moving average MCV charts for detecting increasing MCV shifts, using a linear covariate error model, are investigated. Explicit mathematical expressions are derived to compute the limits and average run lengths of the charts in the presence of measurement errors. Finally, an illustrative example using a real-life dataset is presented to demonstrate the charts’ implementation.     

On properties of probability-based multivariate process capability indices

Multivariate process capability indices (MPCIs) have been proposed to measure multivariate process capability in real-world application over the past three decades. For the practitioner's point of view, the intention of this paper is to examine the performances and distributional properties of probability-based MPCIs. Considering issues of construction of capability indices in multivariate setup and computation with performance, we found that probability-based MPCIs are a proper generalization of univariate basic process capability indices (PCIs). In the beginning of this decade, computation of probability-based indices was a difficult and time-consuming task, but in the computer age statistics, computation of probability-based MPCIs is simple and quick. Recent work on the performance of MPCI NMC_pm and distributional properties of its estimator reasonably recommended this index, for use in practical situations. To study distributional properties of natural estimators of probability-based MPCIs and recommended index estimator, we conducted simulation study. Though natural estimators of probability-based indices are negatively biased, they are better with respect to mean, relative bias, mean square error. Probability-based MPCI MC_pm is better as compared with NMC_pm with respect to performance and as its estimator quality. Hence, in real-world practice, we recommend probability-based MPCIs as a multivariate analogue of basic PCIs.     

A proposed variable parameter control chart for monitoring the multivariate coefficient of variation

An efficient process monitoring system is important for achieving sustainable manufacturing. The control charting technique is one of the most effective techniques to monitor process quality. In certain processes where the process mean and variance are not independent of one another, the coefficient of variation (CV), which measures the ratio of the standard deviation to the mean, should be monitored. In line with industrial settings, where at least two or more variables are monitored simultaneously in most processes, this paper proposes a variable parameter (VP) chart to monitor the multivariate CV (MCV). Formulae and algorithms to optimize the various performance measures are discussed. The proposed VP MCV chart is designed based on a Markov chain approach. The performance comparison shows that the proposed VP MCV chart prevails over the existing MCV charts, in terms of the average time to signal (ATS), standard deviation of the time of signal (SDTS), and expected average time to signal (EATS) criteria. An example is presented to illustrate the implementation of the proposed VP MCV chart.     

New capability indices for evaluating the performance of multivariate manufacturing processes

Generally, an industrial product has more than one quality characteristic. In order to establish performance measures for evaluating the capability of a multivariate manufacturing process, several multivariate process capability indices have been developed in the past few years. Among them, Taam's MC_p and MC_pm indices have the drawback of overestimation and Hubele's three‐component capability vector lacks simplicity in practice. In this article, taking the correlation among multiple quality characteristics into account, we develop two novel indices; NMC_p and NMC_pm. Using two numerical examples we demonstrate that the true performance of multivariate processes are accurately reflected in our NMC_p and NMC_pm indices and in their associated interval estimates. Finally, simulation results show that our indices outperform both those of Taam and Hubele. Copyright © 2009 John Wiley & Sons, Ltd.     

On monitoring of multiple non-linear profiles

Most state-of-the-art profile monitoring methods involve studies of one profile. However, a process may contain several sensors or probes that generate multiple profiles over time. Quality characteristics presented in multiple profiles may be related multiple aspects of product or process quality. Existing charting methods for simultaneous monitoring of each multiple profile may result in high false alarm rates. Or worse, they cannot correctly detect potential relationship changes among profiles. In this study, we propose two approaches to detect process shifts in multiple non-linear profiles. A simulation study was conducted to evaluate the performance of the proposed approaches in terms of average run length under different process shift scenarios. Pros and cons of the proposed methods are discussed. A guideline for choosing the proposed methods is introduced. In addition, a hybrid method combining the salient points of both approaches is explored. Finally, a real-world data-set from a vulcanisation process is used to demonstrate the implementation of the proposed methods.     

Group inspection of dependent binary processes

We consider serially dependent binary processes, how they occur in several fields of practice. If such a process cannot be monitored continuously, because of process speed for instance, then one can analyze connected segments instead, where two successive segments have a sufficiently large time‐lag. Nevertheless, the serial dependence has to be considered at least within the segments, i.e. the distribution of the segment sums is not binomial anymore. We propose the Markov binomial distribution to approximate the true distribution of the segment sums. Based on this distribution, we develop a Markov np chart and a Markov exponentially weighted moving average (EWMA) chart. We show how average run lengths (ARLs) can be computed exactly for both types of chart. Based on such ARL computations, we derive recommendations for chart design and investigate the out‐of‐control performance. A real‐data example illustrates the application of these charts in practice. Copyright © 2008 John Wiley & Sons, Ltd.     

Multivariate variability monitoring using EWMA control charts based on squared deviation of observations from target

Recent research works have shown that control statistics based on squared deviation of observations from target have the ability to monitor variability in both univariate and multivariate processes. In the current research, the properties of the control statistic S _t that has been proposed by Huwang et al. (J. Quality Technology 2007; 39 :258–278) are first reviewed and three new S _t‐based multivariate schemes are then presented. Extensive simulation experiments are performed to compare the performances of the proposed schemes with those of the multivariate exponentially weighted mean squared deviation (MEWMS) and the L₁‐norm distance of the MEWMS deviation from its expected value (MEWMSL₁) charts. The results show that one of the proposed schemes outperforms the others in detecting shifts in correlation coefficients and another has the best general performance among the compared charts in detecting shifts in which at least one of the variances changes. Copyright © 2011 John Wiley & Sons, Ltd.     

Multivariate process incapability vector

Process capability indices evaluate the capability of the processes in satisfying customer's requirements. This paper introduces a superstructure multivariate process incapability vector for multivariate normal processes and then, compares it with four recently proposed multivariate process capability indices to show its better performance. In addition, the effects of two modification factors are investigated. Also, bootstrap confidence intervals for the first component of the proposed vector are obtained. Furthermore, real manufacturing data sets are presented to demonstrate the applicability of the proposed vector.     

生长曲线模型下的统一有偏估计

生长曲线模型有很重要的实际应用背景,针对该模型存在的复共线性问题,本文将统一有偏估计推广到生长曲线模型下,提出多元统一有偏估计。并讨论了它的可容许性,找到了它一致优于最小二乘估计的椭球范围。同样,在实际应用中还会经常遇到带约束的生长曲线模型,针对此时存在着的复共线性问题作了类似的研究,提出了约束多元统一有偏估计。