An online sequential algorithm for the estimation of transition probabilities for jump Markov linear systems

This paper describes a new method to estimate the transition probabilities associated with a jump Markov linear system. The new algorithm uses stochastic approximation type recursions to minimize the Kullback-Leibler divergence between the likelihood function of the transition probabilities and the true likelihood function. Since the calculation of the likelihood function of the transition probabilities is impossible, an incomplete data paradigm, which has been previously applied to a similar problem for hidden Markov models, is used. The algorithm differs from the existing algorithms in that it assumes that the transition probabilities are deterministic quantities whereas the existing approaches consider them to be random variables with prior distributions.     

使用概率规则文法评估人机界面可用性

提出一种在界面系统设计规约的基础上使用的可用性评估方法.首先使用有限状态自动机抽象界面系统设计,根据概率规则文法对有限状态自动机的状态转换概率进行预测;然后结合用户的熟练程度提出了界面可用性评估算法;最后讨论了一个手机界面的可用性计算实例.文中方法能够在界面系统生命周期的早期使用,以较早地对不同设计方案进行比较,降低开发风险.     

软件可靠性评估的重要抽样方法

基于统计测试的马尔可夫使用模型对软件可靠性评估提出了一种有效的估计方法.该方法利用重要抽样技术在保证可靠性估计无偏性的条件下,利用交叉熵度量操作剖面与零方差抽样分布之间的差异,通过启发式迭代过程调整各个状态之间的转移概率来修正测试剖面.从理论上证明了利用修正测试剖面测试估计的可靠性是方差为0的无偏估计.最后给出了软件可靠性估计的最优测试剖面生成的启发式迭代算法.仿真结果表明,该方法与模拟退火算法相比,能够明显降低估计的方差,在提高估计精度的同时加快统计测试速度.     

基于Monte Carlo方法的自适应多模型诊断

多模型混合系统的模型切换服从有限状态的Markov链,其转移概率通常假定是已知的.当模型转移概率未知的时候,本文基于Monte Carlo粒子滤波器给出了混合系统状态估计的一种自适应算法.该算法假定未知的转移概率先验分布为Dirichlet分布,首先通过采样得到一组模型序列的随机样本,利用其中状态的转移次数计算先验转移概率,使用量测信息对样本更新选择后,获得模型转移概率的一种迭代的后验估计值,同时由粒子滤波器得到系统状态和模型概率的后验估计.将该方法用于混合系统的状态监测和故障诊断,仿真结果表明了算法的有效性.     

桌面网格中面向资源可用性预测的任务调度算法

提出桌面网格平台下的一种面向资源可用性预测的任务调度算法.该算法充分考虑了计算资源在执行作业的过程中可能发生的行为,采用预测技术保证了任务的高效而合理的分配.当计算资源发生异常时,通过公平的转移权重预测方法估计资源在下一阶段可能的状态,计算出资源的可靠性概率,然后开始调度子任务给资源.通过建立实验环境,设置不同的可靠性域值T与历史检查资源天数N等参数,在桌面网格上进行了测试.最后把该调度算法的实验结果与PPS等调度策略进行比较,验证了本文的任务调度算法在子任务处理率与通信轮回时间上有比较好的性能.     

Parameter estimation in a model for misclassified Markov data — a Bayesian approach

We estimate parameters in the context of a discrete-time hidden Markov model with two latent states and two observed states through a Bayesian approach. We provide a Gibbs sampling algorithm for longitudinal data that ensures parameter identifiability. We examine two approaches to start the algorithm for estimation. The first approach generates the initial latent data from transition probability estimates under the false assumption of perfect classification. The second approach requires an initial guess of the classification probabilities and obtains bias-adjusted approximated estimators of the latent transition probabilities based on the observed data. These probabilities are then used to generate the initial latent data set based on the observed data set. Both approaches are illustrated on medical data and the performance of estimates is examined through simulation studies. The approach using bias-adjusted estimators is the best choice of the two options, since it generates a plausible initial latent data set. Our situation is particularly applicable to diagnostic testing, where specifying the range of plausible classification rates may be more feasible than specifying initial values for transition probabilities.     

Approximate transient analysis of large stochastic models with WinPEPSY-QNS

In this paper we present a new algorithm for the approximate transient analysis of large stochastic models. The new algorithm is based on the self-correcting analysis principle for continuous-time Markov chains (CTMC). The approach uses different time dependent aggregations of the CTMC of a stochastic model. With the method of uniformization the transient state probabilities of each aggregated CTMC for a time step are calculated. The derived probabilities are used for the construction of stronger aggregations, which are applied for the correction of the transition rates of the previous aggregations. This is done step by step, until the final time is reached. High aggregations of the original continuous-time Markov chain lead to a time and space efficient computational effort. Therefore the approximate transient analysis method based on the self-correcting aggregation can be used for models with large state spaces. For queuing networks with phase-type distributions of the service times this newly developed algorithm is implemented in WinPEPSY-QNS, a tool for performance evaluation and prediction of stochastic models based on queuing networks. It consists of a graphical editor for the construction of queuing networks and an easy-to-use evaluation component, which offers suitable analysis methods. The newly implemented algorithm is used for the analysis of several examples, and the results are compared to the results of simulation runs where exact values cannot be achieved.     

系统调用序列的Markov模型及其在异常检测中的应用

建立了计算机系统中系统调用序列的Markov模型，并在此模型的基础上提出了一种用于计算机异常检测的方法，文章利用统计方法分析进程中系统调用的发生情况，定义了一个依赖于状态转移概率的失配因子，并用它来计算失配率，由此判断被监视进程进行的操作是正常行为还是异常行为，文章还提出了一种基于遗忘因子的状态转移概率的更新算法。     

Incorporating language syntax in visual text recognition with astatistical model

The use of a statistical language model to improve the performance of an algorithm for recognizing digital images of handwritten or machine-printed text is discussed. A word recognition algorithm first determines a set of words (called a neighborhood) from a lexicon that are visually similar to each input word image. Syntactic classifications for the words and the transition probabilities between those classifications are input to the Viterbi algorithm. The Viterbi algorithm determines the sequence of syntactic classes (the states of an underlying Markov process) for each sentence that have the maximum a posteriori probability, given the observed neighborhoods. The performance of the word recognition algorithm is improved by removing words from neighborhoods with classes that are not included on the estimated state sequence. An experimental application is demonstrated with a neighborhood generation algorithm that produces a number of guesses about the identity of each word in a running text. The use of zero, first and second order transition probabilities and different levels of noise in estimating the neighborhood are explored     

安全关键系统的软件可靠性评估方法

基于统计测试的Markov使用链模型对安全关键系统的可靠性估计提出了一种有效的方法。该方法利用重要抽样技术在保证佑计的无偏性条件下,以可靠性估计的方差最小为目的,通过Ali-Silvey距离度量两个分布之间的差异,调整各个状态之间的转移概率分布,修正测试剖面,增加关键操作的遍历概率。最后给出了软件可靠性估计的最优测试剖面生成迭代算法。仿真结果表明,该方法能明显降低估计方差,在提高估计精度的同时能有效地加速统计测试。     

基于情节规则匹配的数据流预测

提出了一种数据流预测算法Predictor.该算法为每个待匹配的一般形式的情节规则分别使用了一个自动机,通过单遍扫描数据流来同时跟踪这些自动机的状态变迁,以搜索每个规则前件最近的最小且非重叠发生.这样不仅将无界的数据流映射到有限的状态空间,而且避免了对情节规则的过于匹配.另外,算法预测的结果是未来多个情节的发生区间和发生概率.理论分析和实验评估表明,Predictor具有较高的预测效率和预测精度.     

Adaptive estimation for a linear system with interrupted observation

This short paper is concerned with the Bayesian estimation problem for a linear system with the interrupted observation mechanism that is expressed in terms of the stationary two-state Markov chain with unknown transition probabilities. Derived is the approximate minimum variance adaptive estimator algorithm coupled with the estimation of the unknown transition probabilities.     

Credal-C4.5: Decision tree based on imprecise probabilities to classify noisy data

In the area of classification, C4.5 is a known algorithm widely used to design decision trees. In this algorithm, a pruning process is carried out to solve the problem of the over-fitting. A modification of C4.5, called Credal-C4.5, is presented in this paper. This new procedure uses a mathematical theory based on imprecise probabilities, and uncertainty measures. In this way, Credal-C4.5 estimates the probabilities of the features and the class variable by using imprecise probabilities. Besides it uses a new split criterion, called Imprecise Information Gain Ratio, applying uncertainty measures on convex sets of probability distributions (credal sets). In this manner, Credal-C4.5 builds trees for solving classification problems assuming that the training set is not fully reliable. We carried out several experimental studies comparing this new procedure with other ones and we obtain the following principal conclusion: in domains of class noise, Credal-C4.5 obtains smaller trees and better performance than classic C4.5.     

双十字搜索算法的快速块匹配运动估计

在块运动估计中,不同形状、不同大小的搜索模型对搜索速度和搜索质量有很大的影响．通过运动矢量概率分布分析,发现了运动矢量概率分布具有除中心十字偏置特性以外的方向性特性,提出了一种快速的双十字搜索（DCS）运动估计算法．该算法首先根据运动矢量概率分布的中心十字偏置性,采用小十字搜索模型（SCSP）和大十字搜索模型（LCSP）对小运动矢量进行搜索,从而减少搜索点数．然后,根据运动矢量概率分布的方向性,使用非完全对称十字搜索模型（NFSCSP）对大运动矢量进行搜索,进一步提高了搜索速度．在保持相当搜索质量的前提下,双十字搜索算法与菱形搜索算法（DS）和十字-菱形搜索（CDS）算法相比,搜索速度分别可提高70％和40％．实验结果证明双十字搜索算法是非常有效的,且具有较强的鲁棒性．     

Multi-aspect target discrimination using hidden Markov models and neural networks

This paper presents a new multi-aspect pattern classification method using hidden Markov models (HMMs). Models are defined for each class, with the probability found by each model determining class membership. Each HMM model is enhanced by the use of a multilayer perception (MLP) network to generate emission probabilities. This hybrid system uses the MLP to find the probability of a state for an unknown pattern and the HMM to model the process underlying the state transitions. A new batch gradient descent-based method is introduced for optimal estimation of the transition and emission probabilities. A prediction method in conjunction with HMM model is also presented that attempts to improve the computation of transition probabilities by using the previous states to predict the next state. This method exploits the correlation information between consecutive aspects. These algorithms are then implemented and benchmarked on a multi-aspect underwater target classification problem using a realistic sonar data set collected in different bottom conditions.     

H∞ control for discrete‐time Markovian jump linear systems with partly unknown transition probabilities

In this paper, the problem of H_∞ control for a class of discrete‐time Markovian jump linear system with partly unknown transition probabilities is investigated. The class of systems under consideration is more general, which covers the systems with completely known and completely unknown transition probabilities as two special cases. Moreover, in contrast to the uncertain transition probabilities studied recently, the concept of partly unknown transition probabilities proposed in this paper does not require any knowledge of the unknown elements. The H_∞ controllers to be designed include state feedback and dynamic output feedback, since the latter covers the static one. The sufficient conditions for the existence of the desired controllers are derived within the matrix inequalities framework, and a cone complementary linearization algorithm is exploited to solve the latent equation constraints in the output‐feedback control case. Two numerical examples are provided to show the validness and potential of the developed theoretical results. Copyright © 2008 John Wiley & Sons, Ltd.     

基于EM的启动子序列半监督学习

启动子的预测对于基因的定位有重要意义.已有多种对启动子进行预测的算法,涉及到信号搜索、内容搜索和CpG岛搜索等多种策略.基于马尔可夫模型的启动子分类方法也有研究,其中的转移概率都是直接通过统计已标号训练样本序列得来的.将半监督学习思想引入启动子序列分析中,推导出转移概率等参数的最大似然估计公式.实验中将待测试基因序列片段同已标号训练样本混合,利用得出的参数值对基因序列片段进行识别,使用少量的已标号的样本数据能得出较好的启动子识别结果.     

基于粒子滤波后验概率分布的多特征融合跟踪

在光照和目标形变等外部条件变化的情况下,仅利用目标的单一特征难以鲁棒的跟踪目标。提出了一种基于粒子滤波后验概率分布的多特征融合跟踪算法,在粒子滤波跟踪框架下,用直方图模型表征目标的颜色和边缘特征,通过两种特征后验概率之间的"协作"与"学习"实现特征融合,各种场景的试验结果比较表明,新的融合跟踪算法比仅用单一特征跟踪、现有的多特征融合算法具有更好的稳定性和鲁棒性,特别是针对环境光照和目标背景变化较大的情况更具有优势。     

模型转移概率自适应的交互式多模型UKF算法

本文给出了一种基于UKF算法的参数自适应交互式多模型方法,较好的解决了非线性条件下机动目标跟踪的问题,可获得比基于EKF算法的交互多模型方法更好的稳定性和计算精度,还避免了复杂的Jacobi矩阵运算;由于该方法结合了模型概率转移自适应技术,实现了对模型转移矩阵的在线估计,降低了人为因素的影响。最后,通过Monte Carlo仿真进一步验证了该方法的正确性和有效性。     

A computational algorithm for design of regulators for linear jump parameter systems

An iterative algorithm is presented for finding the optimal feedback controller for a linear jump parameter System with state-dependent transition probabilities. Conditions guaranteeing convergence are provided, and an example illustrates the application of the algorithm.