用灰色马尔柯夫链预测模型对我国粮食产量的预测

针对粮食生产的不确定性 ,本文提出了用建立在中心逼近式的 GM(1 ,1 )模型及马尔柯夫预测模型上的灰色马尔柯夫预测模型对我国粮食产量进行预测 ,预测建立在对过去年份的数据的分析之上 ,并给出了2 0 0 3年的粮食产量预测结果 .     

上证指数、深圳指数预测的马尔柯夫链预测模糊模型

介绍马尔柯夫链预测法的模糊模型 ,应用模糊模型对证券指数进行预测的实证研究     

利用马尔柯夫预测法预测股市

本文利用马尔柯夫预测法对股市作短期预测,采用常数划分法,突出某一股价进行状态划分。然后建立转移概率矩阵进行预测。     

基于马尔柯夫链预测考研产品市场占有率

在考研产品市场上,欲考研人群对某种考研产品的市场占有率的动态转移变化过程适合马尔柯夫预测模型.本文针对欲考研人群对考研产品的使用情况进行网络问卷调查,共收回有效问卷38182份,利用随机抽样的1000份问卷数据进行预测线下面授考研产品,线上PC端考研产品和线上移动端考研产品的市场占有率,结果表明:多年后,线下面授考研产品市场占有率将稳定在12%,线上PC端考研产品市场占有率将稳定在14%,线上移动端考研产品市场占有率将稳定在74%.通过预测使考研培训机构了解各种考研产品的市场竞争力,采取相应的举措来提高产品的市场占有率.     

销量预测的相对误差马尔柯夫链模糊修正模型研究

在销量预测的改进型灰色预测GM(1,1)模型中,针对传统的相对误差修正的马尔柯夫链预测法,在相对误差微小变化的情况下,存在状态跃变的不合理性,提出应用马尔柯夫链模糊模型的方法.并开展实证研究,得出改进的销量预测值为103.5095万箱的结论.     

一种估算马尔柯夫状态转移概率矩阵的新方法

在现有文献研究的基础上,对马尔柯夫状态转移概率矩阵估算方法又作了进一步研究,根据马尔柯夫状态转移概率矩阵的性质和特点,提出了一种新的估算方法.方法首先构造了一个以相对误差绝对值之和最小为目标,以某一状态转移到其他状态的概率之和等于1以及状态转移概率不小于零为约束条件的优化模型.在此基础上,通过变量替换,将该模型转化为线性规划模型.由于线性规划模型不仅能够求得解析解,而且有现成的求解软件,因此不但便于问题求解,而且更加方便、可靠.最后进行了示例计算,验证了给出的马尔柯夫状态转移概率矩阵优化算法的可行性和正确性.     

灰色-马尔柯夫链预测优化模型——以江苏省物流需求预测为例

讨论了现有灰色-马尔柯夫链预测方法的基本思路,针对该思路的不足之处提出了合理刻画预测模型精度特征的两个精度指标——均值指标和稳定性指标,并据此建立了灰色-马尔柯夫链预测优化模型,最终以江苏省物流需求为例,对该模型进行了实例验证和应用.     

基于改进灰色GM(1,1)马尔柯夫模型的煤矿事故百万吨死亡率预测

煤矿安全事故预防和控制是煤矿安全评价和决策的基础.灰色预测适合于时间短、数据量少和波动不大的系统对象,而马尔可夫链理论适用于预测随机波动大的动态过程.结合灰色预测GM(1,1)模型和马尔可夫链理论的优点,提出了一种改进的灰色马尔可夫GM(1,1)模型.利用改进的GM(1,1)模型进一步拟合煤矿人因失误事故的发展变化趋势,并以此为基础进行马尔柯夫预测,提高预测效果.以2000-2010年全国煤矿事故百万吨死亡率为例进行了预测分析,结果表明模型既能揭示煤矿人因失误事故百万吨死亡率变化的总体趋势,又能克服随机波动性数据对预测精度的影响,具有较强的工程实用性,并对煤矿人因失误安全事故的预测和控制有一定的实际意义.     

马尔柯夫链在多介质环境归趋研究中的应用

以开放的环境系统为依托,阐述了马尔柯夫理论在多介质环境归趋研究中的科学性与合理性,并结合国家重点课题—黄河兰州段典型污染物迁移/转化特性及承纳水平研究,研究壬基酚聚氧乙烯醚(nonylphenol polyethoxylates—NPEOs)在黄河兰州段的环境归趋.研究结果表明,利用马尔柯夫模型可以确定污染物在环境介质间的迁移时间、环境介质内的滞留时间、任意时刻污染物在不同环境介质内的含量、给定时间内不同迁移转化过程的迁移量和降解量、环境系统达到稳定的时间、污染物在环境系统内的稳定分布、以及污染物的环境容量或排放标准.     

灰色-马尔柯夫模型的改进及其参数计算方法

提出了一种改进的灰色-马尔柯夫预测模型,同时给出了状态划分和模型参数计算的方法,应用实例表明了提出的模型和方法是有效的.     

A markov process model for software reliability analysis

Software reliability is a rapidly developing discipline. In this paper we model the fault-detecting processes by Markov processes with decreasing jump intensity. The intensity function is suggested to be a power function of the number of the remaining faults in the software. The models generalize the software reliability model suggested by Jelinski and Moranda (‘Software reliability research’, in W. Freiberger (ed.), Statistical Computer Performance Evaluation, Academic Press, New York, 1972. pp. 465–497). The main advantage of our models is that we do not use the assumption that all software faults correspond to the same failure rate. Preliminary studies suggest that a second-order power function is quite a good approximation. Statistical tests also indicate that this may be the case. Numerical results show that the estimation of the expected time to next failure is both reasonable and decreases relatively stably when the number of removed faults is increased.     

Markov: A methodology for the solution of infinite time horizon markov decision processes

Algorithms are described for determining optimal policies for finite state, finite action, infinite discrete time horizon Markov decision processes. Both value-improvement and policy-improvement techniques are used in the algorithms. Computing procedures are also described. The algorithms are appropriate for processes that are either finite or infinite, deterministic or stochastic, discounted or undiscounted, in any meaningful combination of these features. Computing procedures are described in terms of initial data processing, bound improvements, process reduction, and testing and solution. Application of the methodology is illustrated with an example involving natural resource management. Management implications of certain hypothesized relationships between mallard survival and harvest rates are addressed by applying the optimality procedures to mallard population models.     

A probabilistic algorithm for determining the fundamental matrix of a block M/G/1 markov chain

A simple probabilistic algorithm is given for the determination of the fundamental matrix of a block M/G/1 Markov chain. The algorithm does not require the chain to be positive recurrent. A prima facie case for its value is made with a standard testbench example.     

A quadrature method for a class of strongly oscillatory infinite integrals

A constructive process is presented which leads to a class of quadrature formulas with any preassigned compound precision for the numerical integration of rapidly oscillating functions on (0, ) of the forme ^–x F(x, x), where is a large parameter andF(·,y) is periodic of period unity iny.1980 Mathematics Subject Classification, Primary 41A55, Scondary 41A60     

A K-step look-ahead analysis of value iteration algorithms for Markov decision processes

We introduce and analyze a general look-ahead approach for Value Iteration Algorithms used in solving both discounted and undiscounted Markov decision processes. This approach, based on the value-oriented concept interwoven with multiple adaptive relaxation factors, leads to accelerating procedures which perform better than the separate use of either the concept of value oriented or of relaxation. Evaluation and computational considerations of this method are discussed, practical guidelines for implementation are suggested and the suitability of enhancing the method by incorporating Phase 0, Action Elimination procedures and Parallel Processing is indicated. The method was successfully applied to several real problems. We present some numerical results which support the superiority of the developed approach, particularly for undiscounted cases, over other Value Iteration variants.     

A new method of performance sensitivity analysis for non-Markovian queueing networks

The paper develops a new method of calculating and estimating the sensitivities of a class of performance measures with respect to a parameter of the service or interarrival time distributions in queueing networks. The distribution functions may be of a general form. The study is based on perturbation analysis of queueing networks. A new concept, the realization factor of a perturbation, is introduced for the network studied. The properties of realization factors are discussed, and a set of linear differential equations specifying the realization factors are derived. The sensitivity of the steady-state performance with respect to a parameter can be expressed in a simple form using realization factors. Based on this, the sensitivity can be estimated by applying a perturbation analysis algorithm to a sample path of the system. We show that the derivative of the performance measure with respect to a parameter based on a single sample path converges with probability one to the derivative of the steady-state performance as the length of the sample path goes to infinity. The results provide a new analytical method of calculating performance sensitivities and justifies the application of perturbation analysis algorithms to non-Markovian queueing networks.     

A simpler analysis of a hybrid numerical method for time-dependent convection-diffusion problems

A finite difference method for a time-dependent convection-diffusion problem in one space dimension is constructed using a Shishkin mesh. In two recent papers (Clavero et al. (2005) [2] and Mukherjee and Natesan (2009) [3]), this method has been shown to be convergent, uniformly in the small diffusion parameter, using somewhat elaborate analytical techniques and under a certain mesh restriction. In the present paper, a much simpler argument is used to prove a higher order of convergence (uniformly in the diffusion parameter) than in [2] and [3] and under a slightly less restrictive condition on the mesh.     

A dimension-reduced method of sensitivity analysis for stochastic user equilibrium assignment model

This paper gives a new dimension-reduced method of sensitivity analysis for perturbed stochastic user equilibrium assignment (SUEA) model based on the relation between its Lagrange function and logarithmic barrier function combined with a Courant quadratic penalty term. The advantage of this method is of smaller dimension than general sensitivity analysis and reducing complexity. Firstly, it presents the dimension-reduced sensitivity results of the general nonlinear programming perturbation problem and the improved results when the objective or constraint functions are not twice continuously differentiable. Then it proves the corresponding conclusion of SUEA with smooth or non-smooth cost functions by the method of converting constraint conditions and decision variables. Finally, two corresponding examples (smooth and non-smooth) are given to illustrate the feasibility of this method.     

A convergence analysis of an inexact Newton-Landweber iteration method for nonlinear problem

In this paper, we study the convergence and the convergence rates of an inexact Newton–Landweber iteration method for solving nonlinear inverse problems in Banach spaces. Opposed to the traditional methods, we analyze an inexact Newton–Landweber iteration depending on the Hölder continuity of the inverse mapping when the data are not contaminated by noise. With the namely Hölder-type stability and the Lipschitz continuity of DF, we prove convergence and monotonicity of the residuals defined by the sequence induced by the iteration. Finally, we discuss the convergence rates.     

A convergence analysis of nonlinear implicit iterative method for nonlinear ill-posed problems

Implicit iterative method acquires good effect in solving linear ill-posed problems. We have ever applied the idea of implicit iterative method to solve nonlinear ill-posed problems, under the restriction that α is appropriate large, we proved the monotonicity of iterative error and obtained the convergence and stability of iterative sequence, numerical results show that the implicit iterative method for nonlinear ill-posed problems is efficient. In this paper, we analyze the convergence and stability of the corresponding nonlinear implicit iterative method when α_k are determined by Hanke criterion.