二维水动力弥散的随机数学模型

首先以二维水动力弥散实验为基础 ,根据实验所提供的信息 ,建立了二维水动力弥散实验的随机水质数学模型 .其次我们对所建模型进行了数值模拟计算 ,计算结果与实测数据比较吻合 ,从而说明所建立的随机模型是合理的 ,有实际应用价值 .     

单自由度摩擦系统离散模型

发展了两种随机离散数学模型:导出了一个以二维平均映射描述的随机模型,并建立了一个概率预报模型.通过实例对不同模型进行了比较,对于平均映射模型,分岔图指出了外噪声对系统性质的影响,通过符号动力学方法分析指出概率预报模型的随机性质.     

L\'evy过程驱动的正倒向随机系统的随机最大值原理

研究了由Teugels鞅和与之独立的多维Brown运动共同驱动的正倒向随机控制系统的最优控制问题. 这里Teugels鞅是一列与L\'{e}vy 过程相关的两两强正交的正态鞅 (见Nualart, Schoutens 在2000年的结果). 在允许控制值域为一非空凸闭集假设下, 采用凸变分法和对偶技术获得了最优控制存在所满足的充分和必要条件. 作为应用, 系统研究了线性正倒向随机系统的二次最优控制问题(简记为FBLQ问题), 通过相应的随机哈密顿系统对最优控制 进行了对偶刻画. 这里的随机哈密顿系统是由Teugels鞅和多维Brown运动共同驱动的线性正倒向随机微分方程, 其由状态方程、伴随方程和最优控制的对偶表示共同来构成.     

关于随机环境中的马尔可夫过程的简介——纪念李国平院士吴新谋教授诞辰100周年

该文系统地介绍随机环境中的马尔可夫过程. 共4章, 第一章介绍依时的随机环境中的马尔可夫链(MCTRE), 包括MCTRE的存在性及等价描述; 状态分类; 遍历理论及不变测度; p-θ 链的中心极限定理和不变原理. 第二章介绍依时的随机环境中的马尔可夫过程(MPTRE), 包括MPTRE的基本概念; 随机环境中的q -过程存在唯一性; 时齐的q -过程;MPTRE的构造及等价性定理.第三章介绍依时的随机环境中的分枝链(MBCRE), 包括有限维的和无穷维的MBCRE的模型和基本概念; 它们的灭绝概念;两极分化; 增殖率等.第四章介绍依时依空的随机环境中的马尔可夫链(MCSTRE), 包括MCSTRE的基本概念、构造; 依时依空的随机环境中的随机徘徊(RWSTRE)的中心极限定理、不变原理.     

随机参数和随机资金流环境下基于二次效用函数的投资组合优化

研究完全市场下基于二次效用最大化的带有随机资金流的动态投资组合选择问题,其中假设无风险利率、股票收益率和波动率矩阵都是一致有界随机过程.通过应用线性二次控制方法和向后随机微分方程理论得到了最优投资组合的解析表达式.     

基于可拓变换的二维随机变量分布律的研究

建立了随机事元之间的关系事元,研究随机事件之间的关系.利用关系元刻画了两个随机事件之间的关系.运用可拓变换初步研究了二维离散型随机变量的分布律和边缘分布律的传导分布.     

基于高频词和AUC优化的随机森林文本分类模型

为了针对互联网上的新闻类文档实现对其快速精准地分类,提出一种根据词频先初步降维再进行优化建模的分类思路.先基于高频词汇初步降维,再对降维后的数据以模型AUC值达到最大为目标,采取向后消元的方法构建随机森林分类模型,实现对文档的分类.通过实证分析,发现该方法能够有效地实现对文本的分类,同时减少了建模的运算量,通过AUC值的优化,比单纯依据词频降维构建的随机森林模型分类效果更好.     

二维带跳Navier-Stokes方程解的大偏差原理

在带泊松跳二维随机Navier-Stokes方程解的解的存在唯一性的基础上,利用弱收敛的方法证明了带泊松跳二维随机Navier-Stokes方程解的Freidlin-Wentzell型的大偏差原理.     

基于传导变换的马尔可夫链平稳分布的研究

利用可拓学中的参变量事元描述随机过程,引入了随机过程元的概念,建立了随机过程的可拓模型.利用随机事元刻画随机过程的状态,引入了随机状态元和随机状态元集的概念,给出了马尔可夫事元链模型.利用随机状态元的可拓性以及传导变换对马尔可夫链及其平稳分布进行了初步的拓展研究.     

随机时变线性系统的稳定性

利用构造二次型Ｌｙａｐｕｎｏｖ函数和Ｉｔｏ公式研究了一般ｎ维时变线性Ｉｔｏ型随机微分系统的稳定性,给出了二维时变线性系统的三种常见情形的均方指数 稳定或均方渐近稳定的充分判据。     

Probabilistic verification of a biodiesel production system using statistical model checking

Biochemical system designers are increasingly using formal modelling, simulation, and verification methods to improve the understanding of complex systems. Probabilistic models can incorporate realistic stochastic dynamics, but creating and analysing probabilistic models in a formal way is challenging. In this work, we present a stochastic model of biodiesel production that incorporates an inexpensive test of fuel quality, and we validate the model using statistical model checking, which can be used to evaluate simple or complex temporal properties efficiently. We also describe probabilistic simulation and analysis techniques for stochastic hybrid system (SHS) models to demonstrate the properties of our model. We introduce a variety of properties for various configurations of the reactor as well as results of testing our model against the properties.     

Switching diffusion logistic models involving singularly perturbed Markov chains: Weak convergence and stochastic permanence

Focusing on stochastic dynamics involve continuous states as well as discrete events, this article investigates stochastic logistic model with regime switching modulated by a singular Markov chain involving a small parameter. This Markov chain undergoes weak and strong interactions, where the small parameter is used to reflect rapid rate of regime switching among each state class. Two-time-scale formulation is used to reduce the complexity. We obtain weak convergence of the underlying system so that the limit has much simpler structure. Then we utilize the structure of limit system as a bridge, to invest stochastic permanence of original system driving by a singular Markov chain with a large number of states. Sufficient conditions for stochastic permanence are obtained. A couple of examples and numerical simulations are given to illustrate our results.     

Stochastic observability and applications

In this paper the problem of stochastic observability of alinear system affected by multiplicative white noise and Markovianjumping is investigated. The definition of stochastic observabilityadopted here extends to this framework the definition of thewell known uniform observability of a deterministic time-varyinglinear system. By several examples we show that the conceptof stochastic observability introduced in this paper is lessrestrictive than those introduced in other existing works andit does not always imply stochastic detectability as would beexpected. Finally we prove that this kind of stochastic observabilityallows us to derive a Barbasin–Krasovskii type resultfor exponential stability in mean square. This provides a sufficientcondition which guarantees that any semipositive solution ofcorresponding Riccati differential equation is a stabilizingsolution.     

A stochastic recirculation system with random access

A model for a stochastic recirculation system with randomly accessed multiple heterogeneous servers, no waiting rooms, and exponentially-distributed service times is provided. In this system the units are assigned to one of the servers upon arrival by random mechanism. Units which find all servers busy recirculate and combine with the incoming arrivals and join those already in the system to initiate the next cycle. The equilibrium behavior of the internal and external stochastic processes of the system is analyzed using a two parameter approximation. A simulation model is also developed and its behavior is compared against the analytical model at the steady state. The model with randomly-accessed servers is compared to a single server model already established in the literature. The performance of the model is then examined for a wide range of parameter values to obtain conclusions about its optimal performances.     

一类奇异型平稳随机控制问题

本文研究了一个平稳的奇异型随机控制模型,其状态过程为由随机微分方程生成的扩散过程,这个模型实质性地推广了此前的平稳奇异型随机控制模型．     

Optimal nonlinear feedback control of quasi-Hamiltonian systems

An innovative strategy for optimal nonlinear feedback control of linear or nonlinear stochastic dynamic systems is proposed based on the stochastic averaging method for quasi-Hamiltonian systems and stochastic dynamic programming principle. Feedback control forces of a system are divided into conservative parts and dissipative parts. The conservative parts are so selected that the energy distribution in the controlled system is as requested as possible. Then the response of the system with known conservative control forces is reduced to a controlled diffusion process by using the stochastic averaging method. The dissipative parts of control forces are obtained from solving the stochastic dynamic programming equation. Project supported by the National Natural Science Foundation of China (Grant No. 19672054) and Cao Guangbiao High Science and Technology Development Foundation of Zhejiang University.     

Stochastic Lagrangian Relaxation Applied to Power Scheduling in a Hydro-Thermal System under Uncertainty

A dynamic (multi-stage) stochastic programming model for the weekly cost-optimal generation of electric power in a hydro-thermal generation system under uncertain demand (or load) is developed. The model involves a large number of mixed-integer (stochastic) decision variables and constraints linking time periods and operating power units. A stochastic Lagrangian relaxation scheme is designed by assigning (stochastic) multipliers to all constraints coupling power units. It is assumed that the stochastic load process is given (or approximated) by a finite number of realizations (scenarios) in scenario tree form. Solving the dual by a bundle subgradient method leads to a successive decomposition into stochastic single (thermal or hydro) unit subproblems. The stochastic thermal and hydro subproblems are solved by a stochastic dynamic programming technique and by a specific descent algorithm, respectively. A Lagrangian heuristics that provides approximate solutions for the first stage (primal) decisions starting from the optimal (stochastic) multipliers is developed. Numerical results are presented for realistic data from a German power utility and for numbers of scenarios ranging from 5 to 100 and a time horizon of 168 hours. The sizes of the corresponding optimization problems go up to 200000 binary and 350000 continuous variables, and more than 500000 constraints.     

随机非线性物价模型的最优控制

将政府对价格系统的宏观调控作为外部控制力,建立受控的随机非线性物价模型;利用拟Hamilton系统随机平均法和随机动态规划原理的非线性随机控制策略对系统实施最优控制,控制目标是实现系统的稳定性变大;并通过对比控制前后的Lyapunov指教值说明了控制的有效性.     

两比例失效率元件组成串联系统元件冗余与系统冗余的随机比较

针对两个比例失效率元件组成的串联系统,在热冗余的情形下,讨论了串联系统的元件冗余与系统冗余两种方案,并基于随机序的方法,对普通随机序、失效率序、反失效率序建立了元件冗余优于系统冗余的随机比较理论.