基于移动平均-马尔可夫链模型的上海市机场旅客吞吐量预测研究

为了更多的挖掘数据的信息,本文将移动平均与马尔可夫链模型结合,对马尔可夫链模型进行改进,以拟合结果为动态基准线来构建动态的马尔可夫链,并进行预测及平稳分布研究.利用上海市机场2008年1月至2013年10月的各月数据进行模型拟合,将拟合结果作为动态基准线,进行系统状态划分,确定状态转移概率矩阵.预测未来4个月的中值和区间,并根据马尔可夫链的遍历性和稳定性求出状态的平稳分布.实证表明,该方法的预测精度更高,并且具有较强的适用性和泛化性,具有较强的实用价值.     

基于马尔可夫链的专利产业化概率模型:专利引证的视角

从专利引证的视角,运用数理统计的方法衡量专利在技术发展过程中的重要性和价值,从定量分析入手对专利产业化的概率进行辨析。论证了专利产业化的判别与评价原理。专利引证网络的时间无关性、不可约性以及引证路径单向性满足马尔可夫链收敛的条件。运用马尔可夫链对专利引证网络的随机浏览过程进行建模。建立了专利引证网络与马尔可夫链的对应关系,将专利产业化概率的计算过程转化为计算马尔可夫链转移矩阵稳态分布的过程,设计了转移矩阵最大特征值和特征向量的迭代算法。从美国专利商标局的专利数据库中采集了235项1976年至2006年授权的半导体制造领域电子发射器制造技术的相关专利,分析了专利的年度分布情况。从美国国家经济研究局的专利数据集中查询得到592条专利引证关系,利用网络分析软件Pajek构建专利引证网络。实验结果表明,本文提出专利产业化概率模型切实有效,与专利产业化的实际情况相符,可以作为专利产业化可能性的一种指示。     

Stability of Markov jump systems with quadratic terms and its application to RLC circuits

The paper presents results for the second moment stability of continuous-time Markov jump systems with quadratic terms, aiming for engineering applications. Quadratic terms stem from physical constraints in applications, as in electronic circuits based on resistor (R), inductor (L), and capacitor (C). In the paper, an RLC circuit supplied a load driven by jumps produced by a Markov chain—the RLC circuit used sensors that measured the quadratic of electrical currents and voltages. Our result was then used to design a stabilizing controller for the RLC circuit with measurements based on that quadratic terms. The experimental data confirm the usefulness of our approach.     

基于熵权Markov链的商品零售价格指数预测方法

针对商品零售价格指数的变化特点,提出了一种基于熵权的Markov链预测分析方法。首先用概率均分法对样本数据进行状态等级划分,其次对历史年份商品零售价格指数的状态进行统计,建立不同步长的Markov链状态转移概率矩阵,然后用熵权Markov链分析模型对商品零售价格指数进行预测。最后,通过一个实例说明了该方法的有效性。     

价值链重构与核心价值区转移研究——产业融合方式与效果的比较

产业融合使不同产业的价值链发生解构、重组,价值创造方式和增值能力也因此发生改变。本文从传统产业与传统产业、传统产业与新兴产业、新兴产业与新兴产业的互动融合的角度比较分析三种融合方式产业融合后的价值增值核心区域变迁。研究发现,融合后产业出现价值增高或者价值区转移,并且不同融合方式下差异明显。此结论进一步对产业融合理论和区域价值链理论的拓展研究进行了补充。     

Extended dissipative filtering for Markov jump BAM inertial neural networks under weighted try-once-discard protocol

This paper investigates the problem of extended dissipative filtering for bidirectional associative memory inertial neural networks, where the Markov chain is introduced to describe the switching characteristic in the structure and parameters. Moreover, considering the limited network bandwidth, the weighted try-once-discard protocol, as a significant scheduling mechanism in determining which nodes can be accessed between the sensor nodes and the filter, is employed to avoid the data collisions under the constrained communication channel. The objective of the paper is to develop a filter that can ensure that the filtering error system is stochastically stable with extended dissipative performance. Based on the Lyapunov function and an improved decoupling approach, a set of sufficient conditions satisfying the above objective are derived, and the filter gains are obtained. Finally, an illustrative example is employed to verify the validity of the proposed method.     

淠河流域汛期旱涝变化的周期性与旱涝等级状态预测

淠河流域汛期旱涝灾害频发,严重影响当地的经济发展和居民生命财产安全。研究汛期旱涝灾害演变规律及趋势对指导区域内科学防灾、减灾、救灾具有重要的现实意义。本文将墨西哥帽小波和叠加马尔科夫链分析相结合探讨淠河流域汛期旱涝的演变规律,并对未来年份进行预测。结果表明,在不同时间尺度上流域汛期具有不同的旱涝演变情景,2003年以来在偏涝的背景下发生洪涝灾害的风险大。近50年来淠河流域汛期旱涝演变的第一主周期为2年,第二主周期为9年。叠加马氏链可以准确预测汛期旱涝等级状态,淠河流域汛期涝年各等级出现的概率均大于相应的旱年等级,重现期短于后者。小波分析为马尔科夫链方法提供趋势和背景,马氏链可以实现中短期汛期年景预测,两者互为补充,互为验证,可以提高预测精度和可靠性。     

受控马尔可夫链方法在软件测试上的应用

主要介绍了受控马尔可夫链的基本概念和原理，及受控马尔可夫链方法在软件测试中的应用，并对马尔可夫链的五元素模型进行了描述。     

Beyond vector space model for hierarchical Arabic text classification: A Markov chain approach

The vector space model (VSM) is a textual representation method that is widely used in documents classification. However, it remains to be a space-challenging problem. One attempt to alleviate the space problem is by using dimensionality reduction techniques, however, such techniques have deficiencies such as losing some important information. In this paper, we propose a novel text classification method that neither uses VSM nor dimensionality reduction techniques. The proposed method is a space efficient method that utilizes the first order Markov model for hierarchical Arabic text classification. For each category and sub-category, a Markov chain model is prepared based on the neighboring characters sequences. The prepared models are then used for scoring documents for classification purposes. For evaluation, we used a hierarchical Arabic text data collection that contains 11,191 documents that belong to eight topics distributed into 3-levels. The experimental results show that the Markov chains based method significantly outperforms the baseline system that employs the latent semantic indexing (LSI) method. That is, the proposed method enhances the F1-measure by 3.47%. The novelty of this work lies on the idea of decomposing words into sequences of characters, which found to be a promising approach in terms of space and accuracy. Based on our best knowledge, this is the first attempt to conduct research for hierarchical Arabic text classification with such relatively large data collection.     

Stochastic competitive Lotka–Volterra ecosystems under partial observation: Feedback controls for permanence and extinction

This paper is concerned with Lotka–Volterra models formulated using stochastic differential equations with regime switching represented by a continuous-time Markov chain. Different from the existing literature, the Markov chain is hidden and can only be observed in a Gaussian white noise in our work. For such partially observed problems, we use a Wonham filter to estimate the Markov chain from the observable evolution of the given process, and convert the original system to a completely observable one. We then establish the regularity, positivity, stochastic boundedness, and sample path continuity of the solution. Moreover, stochastic permanence and extinction using feedback controls are investigated. Numerical experiments are conducted to validate the theoretical findings and demonstrate how feedback controls perform in practice.     

A robust global approach for LPV FIR model identification with time-varying time delays

Robust identification of the linear parameter varying (LPV) finite impulse response (FIR) model with time-varying time delays is considered in this paper. A robust observation model based on Laplace distribution is established to deal with the output data contaminated with the outliers, which are commonly existed in modern industries. A Markov chain model is utilized to model the correlation between the time delays as they do not simply change randomly in reality. A transition probability matrix and an initial probability distribution vector are used to govern the switching mechanism of the time delays. Since it is difficult to optimize the complex log likelihood function directly, the derivations of the proposed algorithm are performed under the framework of Expectation-Maximization (EM) algorithm. A numerical example and a chemical process are utilized to verify the effectiveness of the proposed approach.     

A computational study of optimal control of Markov jump systems

This paper considers a class of optimal control problems governed by Markov jump systems. Our focus is to develop a computational method, based on the control parametrization approach, for solving this class of optimal control problems. Due to the existence of the continuous-time Markov chain, the optimal control problem under consideration is a stochastic optimal control problem, and hence the control parametrization technique cannot be applied directly. For this, a derandomization approach is introduced to obtain a representative deterministic optimal control problem. Then, the control parametrization method is applied to obtain an approximate finite dimensional optimization problem which can be computed numerically using the gradient-based optimization method. For this, the gradient formulas of the cost function and the constraint functions with respect to control variables are derived. Finally, a practical application involving a RLC circuit model is solved using the method proposed.     

State estimation for networked systems with Markovian communication constraints and multiple packet dropouts

In this paper, the state estimation problem for discrete-time networked systems with communication constraints and random packet dropouts is considered. The communication constraint is that, at each sampling instant, there is at most one of the various transmission nodes in the networked systems is allowed to access a shared communication channel, and then the received data are transmitted to a remote estimator to perform the estimation task. The channel accessing process of those transmission nodes is determined by a finite-state discrete-time Markov chain, and random packet dropouts in remote data transmission are modeled by a Bernoulli distributed white sequence. Using Bayes’ rule and some results developed in this study, two state estimation algorithms are proposed in the sense of minimum mean-square error. The first algorithm is optimal, which can exactly compute the minimum mean-square error estimate of system state. The second algorithm is a suboptimal algorithm obtained under a lot of Gaussian hypotheses. The proposed suboptimal algorithm is recursive and has time-independent complexity. Computer simulations are carried out to illustrate the performance of the proposed algorithms.     

基于GSCPN的“虚拟工序库”生产系统建模研究与性能分析

为分析"虚拟工序库"生产系统的性能,采用广义随机着色Petri网(GSCPN)基本理论对基于学习率下的"虚拟工序库"生产系统进行建模分析,同时利用广义随机Petri网与马尔可夫链同构的关系,将满足学习率下的"虚拟工序库"生产系统的GSCPN转化为等价的马尔可夫链模型,通过运用GSCPN与马尔可夫链相结合的分析方法对"虚拟工序库"生产系统的性能进行评估分析,并通过实例验证,为"虚拟工序库"生产系统的优化提供依据和参考。     

An event-triggered asynchronous H∞ filtering for singular Markov jump systems with redundant channels

In this paper, the problem of asynchronous H_∞ filtering for singular Markov jump systems with redundant channels under the event-triggered scheme is studied. In order to save the resource of bandwidth limited network and improve quality of data transmission, we utilize event-triggered scheme and employ redundant channels. The redundant channels are modeled as two mutually independent Bernoulli distributed random variables. To formulate the asynchronization phenomena between the system modes and the filter modes, the hidden Markov model is proposed so that the filtering error system has become a singular hidden Markov jump system. The criterion of regular, causal and stochastically stable with a certain H_∞ performance for the filtering error system has been obtained. The co-design of asynchronous filter and the event-triggered scheme is proposed in terms of a group of feasible linear matrix inequalities. Two examples are given to show the effectiveness of the proposed method.     

模块化与产业融合:耦合、机理及效应

通过回顾模块化理论的演进脉络,阐明了模块化与产业融合之间的耦合关系,并由此对模块化作用于产业融合的机理与效应进行了分析。结果表明:只有经过模块化对垂直一体化产业链条的纵向分解与横向整合,产业之间才能够实现更大范围内的有效融合,并最终形成具有新产业属性或新业态的复杂性产业网络,同时这一网络的形成对产业分类、企业边界、市场结构和企业生存法则产生了重要影响。     

Optimal DoS attack scheduling for multi-sensor remote state estimation over interference channels

In this paper, we investigate the optimal denial-of-service attack scheduling problems in a multi-sensor case over interference channels. Multiple attackers aim to degrade the performance of remote state estimation under attackers’ energy constraints. The attack decision of one attacker may be affected by the others while all attackers find their own optimal strategies to degrade estimation performance. Consequently, the Markov decision process and Markov cooperative game in two different information scenarios are formulated to study the optimal attack strategies for multiple attackers. Because of the complex computations of the high-dimensional Markov decision process (Markov cooperative game) as well as the limited information for attackers, we propose a value iteration adaptive dynamic programming method to approximate the optimal solution. Moreover, the structural properties of the optimal solution are analyzed. In the Markov cooperative game, the optimal joint attack strategy which admits a Nash equilibrium is studied. Several numerical simulations are provided to illustrate the feasibility and effectiveness of the main results.     

Dynamical analysis of a stochastic delayed epidemic model with lévy jumps and regime switching

In this paper a delayed stochastic SLVIQR epidemic model, which can be applied for modeling the new coronavirus COVID-19 after a calibration, is derived. Model is constructed by assuming that transmission rate satisfies the mean-reverting Ornstain-Uhlenbeck process and, besides a standard Brownian motion, another two driving processes are considered: a stationary Poisson point process and a continuous finite-state Markov chain. For the constructed model, the existence and uniqueness of positive global solution is proven. Also, sufficient conditions under which the disease would lead to extinction or be persistent in the mean are established and it is shown that constructed model has a richer dynamic analysis compared to existing models. In addition, numerical simulations are given to illustrate the theoretical results.     

基于粗糙集的BP神经网络设计

传统的神经网络对于简单、具有明确分类界限的数据,可以有较好的计算结果,但是在输入属性集合较大、分类界限不明确的情况下,会出现收敛效率和分类准确率较低,甚至会出现不收敛状态。本文利用Rough集的理论,对输入数据不停地进行样本检测,对输入特征不停地进行筛检,以此达到删减输入特征数的目的,从而提高对输入数据的拟合。通过对采集到的脑电信号进行验证,达到删减特征数和提高分类准确度的目的。     

Theoretical and experimental study of through-wall microwave tomography inverse problems

We consider the problem of building structure estimation using microwave ray tomography. A Bayesian formulation is developed, and a Markov chain Monte Carlo (MCMC) procedure is used to sample the posterior distribution, which is based on a data likelihood defined in terms of a residual misfit between observed and predicted waveforms. To accelerate model optimization, a simulated annealing approach is combined with the MCMC, using specific model moves to explore each component of the model space. Our approach is applicable to data acquired in the frequency or time domain and for monostatic or bistatic acquisition modes. Experimental data for a multi-wall laboratory test structure were acquired using a horn antenna connected to a vector network analyzer and used to validate both the forward model and the inversion approach. Although, in true remote sensing problems for building structure, the model order is usually unknown, in this initial study, the actual inversion experiment is performed in a reduced-dimension model space for which a subset of the variables are taken as known or fixed. Generalization to the variable-dimension problem can be achieved by using reversible jump MCMC sampling procedures.