传播动力学模型回顾与展望

传播动力学模型主要包括传染病动力学模型、计算机病毒传播模型以及谣言传播模型.首先从传染病动力学模型入手,分别介绍均匀混合传染病动力学模型和网络动力学模型;然后介绍计算机病毒传播模型以及谣言传播模型,并与传染病模型进行对比.最后作了总结与展望.     

一道华杯赛试题的延伸

<正>在初中数学及数学竞赛中,面积法是解决平面几何问题的利器.正是因为面积法的广泛使用,产生了一些面积模型,例如等高模型,一半模型,燕尾模型,鸟头模型,蝴蝶模型,梯形模型和相似模型等.本文应用蝴蝶模型和鸟头模型研究一道华杯赛试题,帮助同学们体会掌握几何模型对于提升面积法解题能力的优势,     

指向函数模型观念的中考试题研究与启发

初中阶段函数教学要关注数学知识与实际的结合,经历数量关系和变化规律的探究,从选择模型、建立模型、求解模型、验证模型和应用模型的过程中形成函数模型观念.指向函数模型观念的中考试题研究,是数学模型教学开展、学生模型观念形成和新课程标准落地的着力点.     

上海股市波动的预测方式和模型

探讨基于 SV类模型的上海股市波动的预测方式和模型问题 .比较了 SV( stochastic volatility)类模型 (包括基本 SV模型和 ASV模型 )在两种不同方式下的预测效果 ,并将基本 SV类模型的预测效果与 ASV模型 ,以及其他常用模型做了比较 .结果表明 :SV类模型在两种预测方式下的预测效果存在一定的差异 ;基本 SV模型对于上海股市具有较强的预测能力 ;ASV模型的预测效果不理想 .     

基于三类模型的四大银行股票收益率预测研究

运用时间序列分析的预测方法,对四大银行的股票日对数收益率序列进行拟合与预测分析,分别构建ARMA模型、GARCH模型以及ARMA-GARCH组合模型,通过模型比较,实证分析表明:在拟合效果上,ARMA-GARCH模型的拟合优度优于ARMA模型和GARCH模型;在预测效果上,ARMA模型的预测效果最优,ARMA-GARCH模型次之.     

分年龄别生育率概率模型的研究

使用对数正态分布模型、Weibull模型、泊松模型以及由它们构造的两种组合模型对生育率数据进行拟合.仿真结果表明:与单一模型相比,组合模型精度较高,残差平方和较小.并且在全面二孩政策下,组合模型二更加适用.     

一种新的死亡率模型及基于中国人口数据的比较分析

基于中国人口死亡率数据, 对APC模型进行扩展, 并将扩展的死亡率模型(EAPC模型)与APC模型和LC模型进行对比. 通过比较模型的拟合效果和预测效果, 并对其稳定性进行检验, 发现由APC模型扩展而来的EAPC模型更适合于拟合和预测中国的人口死亡率, 这为我国死亡率模型的使用提供了更多可行的方案.     

基于人工智能的黄金价格非线性组合预测

黄金价格时间序列数据具有较强的非线性,在采用单模型对其预测时较多采用线性模型、非线性模型以及加入外生变量的非线性模型.但是,单一模型较难全面体现黄金数据的非线性特征,因此,预测效果不很理想.在利用线性方法进行模型组合时若被组合模型与原数据序列无线性关系,此时采用线性组合预测效果较差,甚至组合后的模型预测精度低于被组合的单模型精度.为充分发挥单一模型的优势,采用人工智能的方法建立非线性组合预测模型,模型可有效利用各模型特点,预测精度优于采用的各单一模型.     

盈余管理计量模型效力的实证研究

本文以随机抽取的上海和深圳证券交易所的1000家被并购上市公司为样本,研究了盈余管理计量模型的检验效力问题。本文通过三种验证各模型效力的实证方法比较了6种盈余管理的计量模型。研究发现:第一类误差的方法认为Healy模型、修正的琼斯模型和前瞻性修正的琼斯模型能有效地检验出盈余管理行为:预测误差标准方法则认为前瞻性修正的琼斯模型是最优的模型:而根据对操纵性应计部分的短期效应的实证则认为Healy模型、修正的琼斯模型、KS模型和前瞻性修正的琼斯模型是较优的计量模型。在综合考虑各种实证结果和中国资本市场实际情况的基础上,认为前瞻性修正的琼斯模型在中国证券市场检验盈余管理行为的效力最强。     

成比例附加故障混合模型

Frailty模型和可加故障模型在研究各种风险因素与疾病发生原因的关系中提供了两种重要的方法.在不知哪个模型更适合数据时,涵盖这两个模型的成比例附加故障模型提供了一个备选模型.Frailty基本上是观察不到的,需要考虑对应的混合模型.本文研究了成比例附加故障混合模型的年龄性质、相依性质.并对该模型中的那些随机变量进行了随机比较.     

VLES turbulence model for an Eulerian–Lagrangian modeling concept for bubble plumes

Traditional Reynolds-averaged Navier–Stokes (RANS) approaches to turbulence modeling, such as the k-ϵ model, have some well-known shortcomings when modeling transient flow phenomena. To mitigate this, a filtered URANS model has been derived where turbulent structures larger than a given filter size (typically grid size) is captured by the flow equations and smaller structures are modeled according to a modified k-ϵ model. This modeling approach is also known as a VLES model (Very Large Eddy Scale model), and provides more details of the transient turbulence than the k-ϵ model at little extra computational cost.In this study a two-phase extension to the VLES model is described. A modeling concept for bubble plumes has been developed in which the bubbles are tracked as particles and the flow of liquid is solved by the Navier–Stokes equations in a traditional mesh based approach. The flow of bubbles and liquid is coupled in an Eulerian–Lagrangian model. Turbulent dispersion of the bubbles is treated by a random walk model. The random walk model depends on an estimation of the eddy life time. The eddy life time for the VLES model differs from a k-ϵ model, and its mathematical expression is derived.The model is applied to ocean plumes emanating from discharge of gas at the ocean floor. Validation with experiments and comparison with k-ϵ model are shown.     

Iterative identification and control design using finite-signal-to-noise models

In this paper, a model is said to be validated for control design if using the model-based controller, the closed loop performance of the real plant satisfies a specified performance bound. To improve the model for control design, only closed loop response data is available to deduce a new model of the plant. Hence the procedure described herein involves three steps in each iteration: (i) closed loop identification; (ii) plant model extraction from the closed loop model; (iii) controller design. Thus our criteria for model validation involve both the control design procedure by which the closed loop system performance is evaluated, and the identification procedure by which a new model of the plant is deduced from the closed loop response data. This paper proposes new methods for both parts, and also proposes an iterative algorithm to connect the two parts. To facilitate both the identification and control tasks, the new finite-signal-to-noise (FSN) model of linear systems is utilized. The FSN model allows errors in variables whose noise covariances are proportional to signal covariances. Allowing the signal to noise ratios to be bounded but uncertain, a control theory to guarantee a variance upper bound is developed for the discrete version of this new FSN model. The identification of the closed loop system is accomplished by a new type of q-Markov Cover, adjusted to accommodate the assumed FSN structure of the model. The model of the plant is extracted from the closed loop identification model. This model is then used for control design and the process is repeated until the closed loop performance validates the model. If the iterations produce no such a controller, we say that this specific procedure cannot produce a model valid for control design and the level of the required performance must be reduced.     

Neural net versus classical models for the detection and localization of leaks in pipelines

Four models of a pipeline are compared in the paper: a nonlinear distributed-parameter model, a linear distributed-parameter model, a simplified lumped-parameter model and an extended neural-net-based model. The transcendental transfer function of the linearized model is obtained by a Laplace transformation and corresponding initial and boundary conditions. The lumped-parameter model is obtained by a Taylor series extension of the transencdental transfer function. Based on the experience of linear models the structure of the neural net model, as an addendum to the nonlinear distributed-parameter model, is obtained. All four models are tested on a real pipeline data with an artificially generated leak.     

A warehouse-based design model for reverse logistics

In this paper, an MILP formulation is proposed for the design of a reverse logistics network based on a warehouse location–allocation model, which optimizes, simultaneously, the forward and reverse networks. A single product model with unlimited capacity is first defined. Subsequently, the model is extended to a multi-product capacitated recovery network model, where capacity limitations and a multi-product system can be considered. The proposed model is compared to published work in the field, where different model assumptions have been proposed. Two cases are described so as to gain a better insight into the model and allow a comparative analysis.     

Non-linear model parameter estimation–estimating a feasible parameter set with respect to model use

This article deals with non-linear model parameter estimation from experimental data. As for non-linear models a rigorous identifiability analysis is difficult to perform, parameter estimation is performed in such a way that uncertainty in the estimated parameter values is represented by the range of model use results when the model is used for a certain purpose. Using this approach, the article presents a simulation study where the objective is to discover whether the estimation of model parameters can be improved, so that a small enough range of model use results is obtained. The results of the study indicate that from plant measurements available for the estimation of model parameters, it is possible to extract data that are important for the estimation of model parameters relative to a certain model use. If these data are improved by a proper measurement campaign (e.g. proper choice of measured variables, better accuracy, higher measurement frequency) it is to be expected that a valid model for a certain model use will be obtained. The simulation study is performed for an activated sludge model from wastewater treatment, while the estimation of model parameters is done by Monte Carlo simulation.     

带有不可忽略缺失数据的广义部分线性模型的贝叶斯分析

广义部分线性模型是广义线性模型和部分线性模型的推广,是一种应用广泛的半参数模型.本文讨论的是该模型在线性协变量和响应变量均存在非随机缺失数据情形下参数的Bayes估计和基于Bayes因子的模型选择问题,在分析过程中,采用了惩罚样条来估计模型中的非参数成分,并建立了Bayes层次模型;为了解决Gibbs抽样过程中因参数高度相关带来的混合性差以及因维数增加导致出现不稳定性的问题,引入了潜变量做为添加数据并应用了压缩Gibbs抽样方法,改进了收敛性;同时,为了避免计算多重积分,利用了M-H算法估计边缘密度函数后计算Bayes因子,为模型的选择比较提供了一种准则.最后,通过模拟和实例验证了所给方法的有效性.     

融合两阶段过程模型和改进Bass模型的网络社交平台上产品信息扩散研究

提出并验证了融合两阶段过程模型和改进Bass模型的网络社交平台上产品信息扩散模型。考虑用户转发动机构建产品信息扩散两阶段过程模型;考虑用户兴趣衰减效应改进Bass模型;融合这两个模型,考虑产品信息发布者明星效应、产品信息质量对产品信息扩散的影响,提出了产品信息扩散模型。以2019年11～12月新浪电影发布的电影预告片转发数据验证了所提模型,并与Bass模型进行了比较。结果表明,用户转发动机和用户兴趣衰减效应对产品信息扩散均有显著影响,所提模型的预测精度和拟合效果均优于Bass模型。所提模型可用于存在不同转发动机及具有衰减效应的其他信息转发量预测,尤其适合于在产品信息投放前期和早期对转发量的预测,是对信息扩散模型的补充。     

A hierarchical optimal control model for co-ordination of functional decisions in a firm

An optimal control model of a joint finance-marketing-production system is formulated. The marketing subsystem in the model is represented by the Nerlove-Arrow model of advertising and the production subsystem is represented by the HMMS model of production planning. Using the model as a reference point, a decentralized procedure for coordinating finance, marketing and production decisions is designed. The procedure appears to have the following two characteristics: (1) a discontinuous discount interest rate and (2) a time-varying transfer price between marketing and production functions. An example is presented to illustrate the procedure. The case of sequential planning of marketing-production and finance decisions is considered and the resultant marketing-production subsystem model is described. A solution procedure is outlined for the subsystem model.     

保险系统中一种推广风险模型的破产概率

将经典复合 Poisson风险模型推广至更为一般情况 ,其中保单以 Poisson分布流到达且收取的保费为随机变量 ,建立一种双复合 Poisson风险模型 .对此模型 ,得到了最终破产概率的一般表达式和破产概率的一个上界估计值 .     

A facility location model for global closed-loop supply chain network design

Forward and reverse supply chains form a closed-loop supply chain. In this paper, a mathematical model is proposed for a closed-loop supply chain network by considering global factors, including exchange rates and customs duties. The model is a multi-objective mixed-integer linear programming model under uncertain demand. A solution approach based on fuzzy programming is developed for solving the optimization problem. The model is then applied in a network, which is located in Southwestern Ontario, Canada. A sensitivity analysis is provided to validate the model. This model considers global factors, multi-objectives, and uncertainty simultaneously in a closed-loop supply chain network.