一个供应链系统的可靠性模型的适定性分析

供应链系统是一个复杂的动态系统,许多影响因素的存在,使得供应链系统具有强烈的随机性,从而直接影响供应链系统的可靠性,我们通过分析供应链系统的状态之间的转移关系,引入增补变量法,用偏微分方程组建立了供应链系统的可靠性模型,并对该模型系统解的存在唯一性进行了讨论和证明.     

加权演化的港口网络模型的研究

港口系统的发展、演化对国民经济的繁荣发展起着至关重要的作用,因此如何定量地描述港口系统的发展、演化规律也就显得尤为关键,由此出发,从整体论的角度把港口系统作为一个整体来进行系统研究,利用复杂网络的特性,提出了一个真实的随时间演化的港口系统发展、演化模型,该模型能够有效地再现实际港口系统的一些统计特性,这些特性对于了解港口系统的运行状态和对于港口容错能力的优化将具有十分重要的意义.     

不含混沌真子系统的Li-Yorke混沌

研究了一类Li-Yorke混沌系统,该系统没有真子系统是Li-Yorke混沌的,我们称之为混沌极小系统.本文证明混沌极小系统是拓扑传递的,而且该系统每个非空开集都包含一个不可数混乱集.混沌极小系统不一定是极小的,本文构造了一个这样的反例.特别地,我们考察了线段连续自映射,指出该类系统都不是混沌极小的,线段上混沌极小子系统的存在性和该系统有正熵是等价的.     

拓扑系统的紧性和分离性

考察拓扑系统的两种紧性——空间式紧和locale式紧,给出紧性的若干刻画,讨论了两种紧性的相互关系,证明了拓扑系统的两种紧性都是拓扑空间紧性的良好推广,说明了紧拓扑系统的闭子拓扑系统、有限和系统以及积系统仍是紧拓扑系统。最后在拓扑系统中考察了紧性加强分离性的问题,得到了紧,(强)T2拓扑系统为(强)T3,(强)T4拓扑系统等结论,并用理想收敛刻画了拓扑系统的强T2分离性。     

具有人为故障的人-机系统的可靠性分析

介绍了一个具有人为故障的人-机系统的可修复模型,利用算子半群理论证明了新模型系统解的存在唯一性和指数型稳定性.另外,当故障率λ_0→∞时,系统的瞬态可用度逼近弱解系统瞬态可用度.即,新模型系统逼近原模型弱解系统.     

基础模糊命题演算系统BL~*的改进系统

基础模糊命题演算系统BL*是一个和基础命题演算系统BL相对独立的命题演算系统。命题演算系统L*是系统BL*的扩张,但不是系统BL的扩张。通过对系统BL*及其它模糊命题演算系统的研究,本文对BL*系统进行了修正,进一步改进了BL*系统中的公理体系。     

带分数阶磁效应的压电梁在有/无热效应时的稳定性

该文考虑了具有分数阶磁效应的一维压电梁系统的适定性及稳定性.首先,通过引入新函数将原系统转换为不含分数阶边界项的等价系统,并利用Lumer-Philips定理证明了该系统的适定性.然后,基于谱分析证得无热效应的压电梁系统的非指数稳定性,并借助Borichev-Tomilov定理^[33]进一步推得系统是多项式稳定的.此外,该文又讨论了有热效应的压电梁系统的适定性,并借助扰动泛函方法证明了压电梁系统在带有热效应时的指数稳定性.     

非线性控制系统的梯度扩张的能达性

研究了仿射非线性控制系统的梯度扩张系统.利用非线性控制系统的微分几何理论,通过计算梯度扩张系统的输出函数沿着输入向量场和系统向量场的李导数,讨论仿射非线性控制系统的梯度扩张系统的能达性分布,研究了非线性控制系统和它的梯度扩张系统的能达性之间的关系,证明了如果梯度扩张系统是能达的,则原非线性控制系统也是能达的.     

带有时滞的离散多智能体系统的H_∞一致性问题

考虑带有时滞的离散多智能体系统的H_∞一致性问题,采用增广系统法将原系统转换成为一个不带时滞的降阶系统.通过利用李雅普诺夫稳定性理论研究降阶系统的稳定性,得到多智能体系统达到H_∞一致的线性矩阵不等式形式的条件.最后,仿真结果验证理论结果的有效性.     

具有止步和中途退出的两不同服务台的可修排队

研究了等待空间有限的两服务台可修排队系统,其中一个服务台可能故障.到达的顾客可能进入系统也可能不进入系统(止步),进入系统的顾客可能因等待的不耐烦而中途退出.利用马尔可夫过程的方法建立了系统稳态概率满足的方程组,通过分块矩阵推导出了系统稳态概率向量的迭代计算公式,由此得到了系统各项性能指标的计算公式.最后,给出了一些数值结果.     

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.     

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.     

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.     

ETSK模糊模型辨识及模糊控制算法

本文提出一种基于扩张原理的ＥＴＳＫ（ＥｘｔｅｎｄｅｄＴＳＫ）模型,导出了该模型的输入输出解析式,给出了辨识这种模型的方法。本文还导出了ＥＴＳＫ模型的一种等价形式——变权ＴＳＫ模型,从而将ＥＴＳＫ模型规则后件中的模糊数及其扩展运算转化为普通数的运算,使基于ＥＴＳＫ模型的模糊控制算法ＭＢＦＣ（Ｍｏｄｅｌ－ＢａｓｅｄＦｕｚｚｙＣｏｎｔｒｏｌ）易于实现。仿真辨识结果表明,ＥＴＳＫ模型的辨识效果和预报精度优于ＴＳＫ和ＬＭ模型;ＭＢＦＣ算法的控制效果优于通常模型ＰＩ控制算法     

THE PERIODIC SOLUTIONS FOR TIMEDEPENDENT AGE-STRUCTURED POPULATION MODELS

1IntroductionTl1eage-structuredpopulationmodelhasbeensuccessfullyusedinpopulationdynamics['f'].Tl1egeneralage-structuredpopuIationmodelisdescribedbythefollowingpartialdifferentialequatiolls{3]:whereu(a,f)istheage-specificdensityofasingleage-structuredspeciesattimet,p(a,f)andb(a,t)aretheage-specificpercapitaldeathrateandbirthrateofthespeciesattimet,respectively.TherealAisthemaxinlulllageforanyindividuali11thepopulation,and[A1,A2listhefecundityperiodoffelllales.Thefunctiolluo(a)istheinitialdel…     

A preferential attachment model with Poisson growth for scale-free networks

We propose a scale-free network model with a tunable power-law exponent. The Poisson growth model, as we call it, is an offshoot of the celebrated model of Barabási and Albert where a network is generated iteratively from a small seed network; at each step a node is added together with a number of incident edges preferentially attached to nodes already in the network. A key feature of our model is that the number of edges added at each step is a random variable with Poisson distribution, and, unlike the Barabási–Albert model where this quantity is fixed, it can generate any network. Our model is motivated by an application in Bayesian inference implemented as Markov chain Monte Carlo to estimate a network; for this purpose, we also give a formula for the probability of a network under our model.     

Ruin theory in the linear model

The probability of ruin is examined in a model where the annual gains of an insurance company are dependent random variables. The model used is the linear model (well known in time-series analysis) which includes the autoregressive model and the moving average model as special cases. It is also shown that a certain credibility model can be interpreted as a first-order model of the mixed type.     

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 .     

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。