一种基于非单调逻辑的模型管理方法

本文讨论了模型管理中不确定性的表达、传递、证据合成以及问题求解过程,提出了一种 基于非单调逻辑的模型管理方法:将模型结构形式的不确定性表示为由建模者或领域专家对 问题结构中未知或随机情形所作假设集支持的可能性命题;模型之间不确定性关系的管理通 过对假设环境的真值(一致性)保持和信度调整过程来实现,其依据是在问题求解过程中出现 的冲突情形或者是由决策人提供的有关命题或次判断.     

温室环境神经网络建模

温室环境的生产过程具有许多特点，时变性、非线性和不确定性，很难建立精确的数学模型；利用人工神经网络对温室环境温度进行建模；通过输入样本数据对神经网络模型进行训练，确定网络结构；实验结桌表明，模型中的输入和输出时延不同，模型的精度也不相同；最后，确定了一种适用模型。     

基于非参数密度估计的不确定轨迹预测方法

随着大量移动设备的出现，准确和高效的轨迹预测有助于提高面向位置的应用和服务的质量和水平.针对现有方法对轨迹不确定性缺乏有效建模的问题，提出了基于非参数密度估计的不确定轨迹终点预测方法.在轨迹建模及模型训练阶段，利用非参数估计对起点与终点相同的轨迹构建基于密度分布的不确定轨迹模型；在轨迹预测阶段，将待预测轨迹视为轨迹数据流，并通过KS（Kolmogorov-Smirnov）检验方法与具有相同起点的不确定轨迹模型进行匹配，其中匹配程度最高的不确定轨迹即为预测轨迹.通过真实轨迹数据集上的实验表明，与现有各类主要轨迹预测方法相比，本方法在不同条件下的预测效率与准确性都有较明显优势.     

具有可参数化不确定性系统的对偶自适应模型预测控制

控制系统中存在的不确定性为其性能优化带来诸多问题.自适应控制和鲁棒控制是针对系统存在的不确定性而采取的不同设计策略;前者没有充分考虑系统的未建模动态,而后者往往是针对不确定的最大界而设计,具有较强的保守性.本文试图将自适应控制和鲁棒控制的策略相结合,提出了一种在模型预测控制中利用未来不确定信息的对偶自适应模型预测控制策略.该策略将系统中由未建模动态引起的不确定性参数化表达,并为其设定边界约束,作为优化问题中新的约束,在优化控制目标的同时减小系统不确定性对控制的影响.仿真结果表明,本文提出的算法较传统自适应模型预测控制算法,对于系统存在的不确定性由于在迭代过程中采用参数化描述,得到了更好的系统性能,且具有更好的收敛性.     

基于LMI方法的网络闭环控制系统离散鲁棒模糊控制

针对网络闭环控制系统中时延和不同步等不确定因素,将时延的不确定性转换为系统状态方程系数矩阵的不确定性,提出了一种新的网络闭环控制系统建模方法———具有时滞的不确定离散模糊T-S模型;并在此模型的基础上,利用并行分布补偿原理和Lyapunov理论及LMI方法,证明了通过状态的静态反馈模糊控制,使闭环系统稳定的充分条件等价于求解一组LMI。仿真示例验证了该控制方法的有效性.     

基于LMI方法的网络闭环控制系统离散鲁棒模糊控制

针对网络闭环控制系统中时延和不同步等不确定因素,将时延的不确定性转换为系统状态方程系数矩阵的不确定性,提出了一种新的网络闭环控制系统建模方法--具有时滞的不确定离散模糊T-S模型并在此模型的基础上,利用并行分布补偿原理和Lyapunov理论及LMI方法,证明了通过状态的静态反馈模糊控制,使闭环系统稳定的充分条件等价于求解一组LMI.仿真示例验证了该控制方法的有效性.     

基于高斯模型的不确定数据流Skyline查询方法

由于数据的动态性及不确定性等特征,使得不确定数据流上Skyline查询研究面临挑战.不确定对象一般采用多元概率密度函数(PDF)表示,现有的不确定数据流Skyline查询方法均采用离散型随机变量建模.然而不确定数据流中的对象可能是连续变化的,离散模型对连续性随机变量难以适用.针对连续PDF建模的不确定数据流Skyline查询进行了研究,提出了基于高斯模型的不确定数据流Skyline查询方法(SGMU),该方法包含2个过程:1)动态高斯建模算法(DGM):对滑动窗口采样并建立高斯模型,将原始的数据流转化为不确定对象PDF的参数流;2)提出了基于高斯树的查询算法(GTS)以建立空间索引结构和执行Skyline查询.实验结果表明,SGMU算法不仅能够对连续型不确定对象进行有效建模以辅助Skyline查询,而且能够有效地减少查询对象个数,提高Skyline查询效率.     

网络控制系统H∞鲁棒控制器设计

针对存在不确定时延的网络控制系统, 将未知扰动和建模误差转换为满足给定约束的矩阵, 建立具有参数不确定性的网络控制系统模型。基于Lyapunov稳定性理论证明控制系统渐近稳定, 结合线性矩阵不等式完成H∞鲁棒控制器设计。通过仿真实验, 比较在不同时延条件下系统的状态响应曲线, 结果证明所设计的H∞鲁棒控制器可以解决系统中存在不确定建模误差、干扰和时延等问题, 具有一定的鲁棒性。     

.基于知识系统的不确定性模型

研究基于知识系统（KBS）中知识的不确定性是开发KBS的重要问题,但现有模型化KBS几乎都是基于确定性知识的。以经典的CommonKADS模型为背景,采用模型化工程中的不确定性技术,研究KBS中不确定性知识的表示方法。首先在基于值系统的值集概念上引入假设函数集合的评估函数,定义静态不确定性领域知识;而后采用因果模型描述动态的不确定性推理知识和任务知识;最后将三类不确定性知识映射至CommonKADS模型。由此给出了描述不确定KBS的通用模型,扩展了KBS的可用性。     

基于云模型以约束为中心的复杂系统定性建模

针对以约束为中心的复杂系统仿真中缺乏有效的不确定性信息描述方法,导致仿真中不确定性信息不能充分利用的问题,借鉴和采纳云模型的相关理论和方法,研究了以约束为中心基于云模型的复杂系统定性建模方法。首先提出了仿真中基于云模型的不确定信息表示方法、基于云模型和群体专家决策的量空间构建方法;然后给出了基于云模型的系统定性约束方程的构建方法;最后将提出的建模方法应用于一个敏捷供应链的建模实例。结果表明提出的建模方法具有客观表达不确定信息、将定性与定量信息在仿真中相互融合的优点。     

具有传感器增益退化、随机时延和丢包的分布式融合估计器

研究了具有传感器增益退化、模型不确定性、数据传输时延和丢包的网络化多传感器分布式融合估计问题,模型的不确定性描述为系统矩阵受到随机扰动,传感器增益退化现象通过统计特性已知的随机变量来描述,随机时延和丢包现象存在于局部最优状态估计向融合中心传输的过程中.首先,设计了一种局部最优无偏估计器,然后将传输时延描述为随机过程,并在融合中心端建立符合存储规则的时延-丢包模型,利用最优线性无偏估计方法,导出最小方差意义下的分布式融合估计器.最后,通过算例仿真证明所设计融合估计器的有效性.     

Model uncertainty: theory and practical implications

A model is a representation of a system that can be used to answer questions about the system. In many situations in which models are used, there exists no set of universally accepted modeling assumptions. The term model uncertainty commonly refers to uncertainty about a model's structure, as distinguished from uncertainty about parameters. This paper presents alternative formal approaches to treating model uncertainty, discusses methods for using data to reduce model uncertainty, presents approaches for diagnosing inadequate models, and discusses appropriate use of models that are subject to model uncertainty     

Regional Air Quality Conformity in Transportation Networks with Stochastic Dependencies: A Theoretical Copula-Based Model

The air quality levels in various regions around the world remain a large public concern. Transportation is known to be a major contributor to reduced air quality levels. Until now, the modeling of the regional impact of transportation on air quality has been based on the assumption of determinism. On the other hand, it is well recognized that transportation systems are subject to both demand and supply uncertainties. In this paper, we relax the assumption of determinism and allow for capacity and link flow uncertainty. We introduce a probability measure – coined the conformity probability – to capture the full probabilistic behavior of vehicular emissions. Moreover, stochastic dependencies are modeled using copulas, generalizing other commonly used dependence modeling techniques in the transportation network modeling arena. In a case study we demonstrate that such a generalization is critical as the ranking of capacity expansion projects to improve air quality is shown to be dependent on the hypothesized dependence structure. Finally, we present some preliminary results that suggest that capacity uncertainty is more detrimental to the environment (i.e. leads to lower conformity probabilities) than demand uncertainty.     

Online monitoring by dynamically refining imprecise models

Model-based monitoring determines faults in a supervised system by comparing the available system's measurements with a priori information represented by the system's mathematical model. Especially in technical environments, a monitoring system must be able to reason with incomplete knowledge about the supervised system, to process noisy and erroneous observations and to react within a limited time. We present MOSES, a model-based monitoring system which is based on imprecise models where the structure is known and the parameters may be imprecisely specified by numerical intervals. As a consequence, only bounds on the trajectories can be derived with imprecise models. These bounds are computed using traditional numerical integration techniques starting from individual points on the external surface of the model's uncertainty space. When new measurements from the supervised system become available, MOSES checks the consistency of this new information with the model's prediction and refutes inconsistent parts from the uncertainty space of the model. A fault in the supervised system is detected when the complete model's uncertainty space has been refuted. MOSES bridges and extends methodologies from the FDI and DX communities by refining the model's uncertainty space conservatively through refutation, by applying standard numerical techniques for deriving the trajectories of imprecise models and by exploiting the measurements as soon as possible for online monitoring. The performance of MOSES is evaluated based on examples and by online monitoring a complex heating system.     

基于OMCM的仿真系统概念建模方法

针对仿真系统概念模型开发中存在的模型重用性不高和缺乏管理等问题,提出了元概念模型(Meta Concep-tual Model,MCM)的概念,以实现更高层次上的概念模型抽象。将本体思想引入MCM的设计中,提出了基于本体的元概念模型(Ontology-based MCM,OMCM)概念,并给出了OMCM的层次结构和建模方法。通过将OMCM和概念模型进行映射,实现了基于OMCM的概念模型建模。最后,将该方法应用于装备保障仿真系统概念模型的建模中,起到了很好的效果。     

基于模糊控制的某教练机飞行姿态控制器设计

提出了一种基于规则的模糊逻辑飞行控制系统的设计方法,用以解决某教练机训练系统中数学模型时变性和不确定性问题.为了避免建模的困难,某教练机飞控系统采用模糊逻辑控制设计其控制律,结合飞行员的操纵经验,对系统进行动态调整.以俯仰角为研究对象,利用MATLAB中的fuzzy工具箱实现了模糊控制器设计,给出了俯仰角模糊控制器的控制曲面视图,并在SIMULINK仿真环境下建立了仿真模型.结果表明,所设计的模糊逻辑控制器满足操作品质的要求,具有较好的鲁棒性,对教练机驾驶训练仿真平台的飞行控制系统设计具有一定的应用价值.     

An investigation of the economic perspective in modeling occupational risk

The existing approaches to modeling occupational risk assume that the goals of the enterprise and the worker are identical and that they are served through a mutually optimal performance level. This paper aims to challenge this assumption and indicate the implications in the absence of it. This discussion is conducted by focusing on the economic perspective through the application of utility analysis in workplace risk. Different cases of employment status are examined with special reference to rational and biased decision‐making under uncertainty. This analysis does not offer a new alternative to risk modeling on its own, but it can offer some important insight into this process. © 2010 Wiley Periodicals, Inc.     

Exploring uncertainty and model predictive performance concepts via a modular snowmelt-runoff modeling framework

Model selection is an extremely important aspect of many hydrologic modeling studies because of the complexity, variability, and uncertainty that surrounds the current understanding of watershed-scale systems. However, development and implementation of a complete precipitation-runoff modeling framework, from model selection to calibration and uncertainty analysis, are rarely confronted. This paper introduces a modular precipitation-runoff modeling framework that has been developed and applied to a research site in Central Montana, USA. The case study focuses on an approach to hydrologic modeling that considers model development, selection, calibration, uncertainty analysis, and overall assessment. The results of this case study suggest that a modular framework is useful in identifying the interactions between and among different process representations and their resultant predictions of stream discharge. Such an approach can strengthen model building and address an oft ignored aspect of predictive uncertainty; namely, model structural uncertainty.     

Extended possibilistic truth values

A problem with the modeling of uncertainty within the context of an information or knowledge‐based system is the handling of missing information. In this article, extended possibilistic truth values are introduced as a formal means to cope with this problem. The notion of an extended possibilistic truth value has been obtained from the assumption that the truth value of a proposition can be undefined. This is the case if the proposition cannot be evaluated due to the non‐applicability of (some of) its elements. By definition, an extended truth value can either be true, false, or undefined. Using these three values, a ternary strong Kleene propositional logic has been built. An uncertainty model for this logic is proposed, in order to model (linguistic) uncertainty concerning the extended truth value of a proposition. This uncertainty model is based on possibility measures, and leads to the concept of an extended possibilistic truth value. Finally, the algebraic properties of extended possibilistic truth values are presented. © 2002 Wiley Periodicals, Inc.     

A computational approach to approximate and plausible reasoning with applications to expert systems

The intended purpose of this paper is twofold: proposing a common basis for the modeling of uncertainty and imprecision, and discussing various kinds of approximate and plausible reasoning schemes in this framework. Together with probability, different kinds of uncertainty measures (credibility and plausibility functions in the sense of Shafer, possibility measures in the sense of Zadeh and the dual measures of necessity, Sugeno's g?-fuzzy measures) are introduced in a unified way. The modeling of imprecision in terms of possibility distribution is then presented, and related questions such as the measure of the uncertainty of fuzzy events, the probability and possibility qualification of statements, the concept of a degree of truth, and the truth qualification of propositions, are discussed at length. Deductive inference from premises weighted by different kinds of measures by uncertainty, or by truth-values in the framework of various multivalued logics, is fully investigated. Then, deductive inferences from imprecise or fuzzy premises are dealt with; patterns of reasoning where both uncertainty and imprecision are present are also addressed. The last section is devoted to the combination of uncertain or imprecise pieces of information given by different sources. On the whole, this paper is a tentative survey of quantitative approaches in the modeling of uncertainty and imprecision including recent theoretical proposals as well as more empirical techniques such as the ones developed in expert systems such as MYCIN or PROSPECTOR, the management of uncertainty and imprecision in reasoning patterns being a key issue in artificial intelligence.