基于证据推理的混合型多指标群组决策方法

针对决策方案的属性值为语言评价等级和区间灰数的灰色多指标群组决策问题,提出一种基于证据推理的灰色多指标群组决策方法.首先,根据语言评价信息的概率分布和效用值等价原理确定定性指标和定量指标的信用结构,进而得到群体等级信用结构决策矩阵,然后,依据证据推理方法,对群组评价信息进行合成,求出各方案在各等级的信任度,最后,利用期望方差排序方法确定整个方案集的排序.具体算例表明方法合理有效.     

基于MYCIN不确定因子的区间灰数应急决策方法

主要研究了方案的指标值为区间灰数的快速应急决策方法。将MYCIN不确定因子融合到灰色决策理论中,通过计算各方案在不同指标下的MYCIN不确定因子并对其进行融合,确定最佳决策方案。建立了基于证据推理的应急决策方法,给出了区间灰数决策方法步骤;通过案例分析结果验证了所提方法的有效性。     

基于集对分析的多种不确定偏好形式大群体决策方法

针对具有多种不确定偏好形式的多方案大群体决策问题,提出一种基于集对分析的群决策方法。将区间数、三角模糊数以及语言值三种形式的不确定偏好转换为联系数,保留了不确定偏好信息中的确定性与不确定性。提出一种区间聚类算法,在决策成员权重未知的情况下对成员进行赋权。利用加权综合联系数对大群体偏好进行集结,根据方案的集对势大小给出方案的排序。该方法避免了确定权重时的主观性,同时考虑决策信息的确定性与不确定性,提高了决策结果的可信度。通过实例分析验证了方法的有效性和实用性。     

基于云模型和证据推理的生态环境安全评价

文章提出了一种生态环境安全综合评价模型。首先构建生态环境安全评价指标体系并划分等级区间, 通过云模型将各指标实际值转化为生态环境安全等级的关联度; 其次结合证据推理与熵权法动静态组合确定指标权重; 然后利用证据推理计算出各指标基本可信度分配函数, 再采用证据融合算法合成出生态环境安全综合评估概率分布; 最后根据“最大关联度准则”得出评价结果。以湖南省为例开展实例研究, 研究结果与模糊综合评价法评估等级结果一致, 验证了所提出方法具有合理性、可行性和有效性。     

一种基于组合不确定型OWA算子的区间数群决策方法

在群决策中,由于决策环境的不确定性,决策者给出区间效信息.基于区间数可能度矩阵公式和互补判断矩阵的排序公式,提出了一种组合不确定型OWA算子,它是不确定型OWA算子的推广.该算子能集结群决策中区间数信息,文中给出了其在应用的具体步骤,最后实例分析说明了该方法的有效性和可行性.     

基于指标权重分配的群决策赋权方法研究

本文的主要工作是从指标层入手对群决策中的赋权方法进行研究。首先,根据多位专家给出的某一指标的多个权重,通过一致性检验确定该指标的合理取值区间;然后,以组合权重与通过一致性检验的专家权重之间的偏差最小为目标函数建立优化模型,求解组合权重;最后,通过算例验证了模型的有效性和可行性。本文的主要创新与特色有两点:一是从指标层面对专家权重的一致性进行检验,与根据专家的权重向量的一致性检验相比,更加灵活,且避免了对有效信息的删除和无效信息的放大;二是从指标层面确定指标的组合权重,解决了群决策中的组合赋权问题,改变了专家权重分配对组合权重的影响问题。     

毕达哥拉斯群决策方法及其在绿色供应商选取中的应用

针对专家权重未知且属性值为毕达哥拉斯模糊数的多属性群决策问题,基于证据理论和混合加权毕达哥拉斯MSM算子,提出了一种群决策方法。 首先,由决策信息矩阵获取专家的模糊测度,并赋予其相应的权重;其次,基于新构造的混合加权毕达哥拉斯MSM算子对专家所提供的属性信息分别进行集结,得到各个专家的综合评价信息;再次,利用证据合成方法,对专家综合评价信息进行融合,获得候选方案的综合证据信息,进而可知备选方案的信任区间,并据此对候选方案进行优选决策;最后,绿色供应商选取案例的分析与对比验证了方法的可行性与合理性。     

基于证据推理的不完全信息决策方法研究及应用

提出了一种基于证据推理和优化模型的不完全信息决策方法。针对专家认知偏好的多样性以及决策问题的复杂性特点,提出了一类评价指标不尽相同的不完全信息决策问题;运用证据理论中的基本信度分配来描述专家意见,给出了此类问题的信度函数、似真度函数、合成法则和不同专家贡献度的定义,计算了各个指标的基本信度分配值;从最大程度保持专家原始判断偏好的角度,建立了指标权重确定的优化模型;文后以商用飞机成本管控风险的重要性评价为例,说明了方法的应用步骤和有效性。     

三参数区间灰数的多属性灰关联决策方法

针对决策信息不确定的多属性决策问题,利用三参数区间灰数的概率分布特征及经典灰关联决策的优势,提出了基于三参数区间灰数的灰关联决策方法.首先定义了三参数区间灰数决策向量与理想最优方案和临界方案决策向量的区间关联系数,其次得到所有方案决策向量的区间综合关联度,由区间综合关联度最大化和灰熵最大化确定属性的权重,进而对方案进行择优排序,最后用算例说明决策模型的合理性和实用性.     

基于集成算子改进得分函数的区间直觉模糊多属性决策方法

为更大程度的保留决策信息的原始性，针对决策过程决策信息的聚合、备选方案的比选问题，提出一种基于集成算子改进得分函数的区间直觉模糊多属性决策方法。首先，构建各决策者区间直觉模糊集评分矩阵，并根据模糊熵获得各决策者权重。其次，利用区间模糊集集成算子得到区间直觉模糊综合决策矩阵，进而选择Hamming距离表示方法，建立总离差最大化为目标的最优化模型客观确定属性权重。然后，基于得分函数的定义及性质将原始得分函数进行改进，获得各方案的得分区间矩阵，并将其与决策者属性进行综合得到综合得分区间。最后，根据区间数中心和半径的全序关系对方案的距离，计算每个方案的最终得分，并通过某公司选择投资企业算例验证该方法的可行性和有效性。     

Uncertainty quantification of multi-dimensional parameters for composite laminates based on grey mathematical theory

In order to perform uncertainty quantification of elastic mechanical properties for composite laminates with multi-dimensional parameters, this paper is to develop a novel quantification approach based on grey mathematical theory. Here, uncertain parameters are modeled as correlated interval variables by virtue of some limited experimental points. The developed method not only can eliminate big errors in experimental points, but also can estimate uncertain information including nominal values, uncertain intervals, auto and mutual uncertainties of elastic properties. Besides, it can give out feasible domains of mechanical properties when considering mutual uncertainties for uncertainty propagation analysis. The numerical examples are implemented to demonstrate the feasibility and availability of the developed method. The results show that the developed method can become an important and powerful tool for uncertainty quantification of composite laminates with mutual uncertainties.     

Time response of structure with interval and random parameters using a new hybrid uncertain analysis method

Practical structures often operate with some degree of uncertainties, and the uncertainties are often modelled as random parameters or interval parameters. For realistic predictions of the structures behaviour and performance, structure models should account for these uncertainties. This paper deals with time responses of engineering structures in the presence of random and/or interval uncertainties. Three uncertain structure models are introduced. The first one is random uncertain structure model with only random variables. The generalized polynomial chaos (PC) theory is applied to solve the random uncertain structure model. The second one is interval uncertain structure model with only interval variables. The Legendre metamodel (LM) method is presented to solve the interval uncertain structure model. The LM is based on Legendre polynomial expansion. The third one is hybrid uncertain structure model with both random and interval variables. The polynomial-chaos-Legendre-metamodel (PCLM) method is presented to solve the hybrid uncertain structure model. The PCLM is a combination of PC and LM. Three engineering examples are employed to demonstrate the effectiveness of the proposed methods. The uncertainties resulting from geometrical size, material properties or external loads are studied.     

Adaptive robust synchronization of Rossler systems in the presence of unknown matched time-varying parameters

This paper deals with the problem of adaptive robust synchronization of chaotic systems based on the Lyapunov theory. A controller is designed for a feedback linearizable error system with matched uncertainties. The proposed method shows that the drive and response systems are synchronized and states of the response system track the states of the drive system as time tends to infinity. Since this approach does not require any information about the bound of uncertainties, this information is not needed in advance. In order to prevent the frequent switching phenomenon in the control signal, the method is modified such that the norm of tracking error is bounded. Numerical simulations on two uncertain Rossler systems with matched uncertainties show fast responses of tracking error, while the errors are Uniformly Ultimately Bounded, and the control signal is reasonably smooth.     

A unified method and its application to brake instability analysis involving different types of epistemic uncertainties

The key idea of the proposed method is the use of the equivalent variables named as evidence-based fuzzy variables, which are special evidence variables with fuzzy focal elements. On the basis of the equivalent variables, an uncertainty quantification model is established, in which the unified probabilistic information related to the uncertain responses of engineering systems can be computed with the aid of the fuzziness discretization and reconstruction, the belief and plausibility measures analysis, and the interval response analysis. Monte Carlo simulation is presented as a reference method to validate the accuracy of the proposed method. The proposed method then is extended to perform squeal instability analysis involving different types of epistemic uncertainties. To illustrate the feasibility and effectiveness of the proposed method, seven numerical examples of disc brake instability analysis involving different epistemic uncertainties are provided and analyzed. By conducting appropriate comparisons with reference results, the high accuracy and efficiency of the proposed method on quantifying the effects of different epistemic uncertainties on brake instability are demonstrated.     

Matrix completion for cost reduction in finite element simulations under hybrid uncertainties

In recent years, uncertainty appears in different aspects of physical simulations including probabilistic boundary, stochastic loading, and multiscale modeling. Stretching across engineering domains and applied mathematics, uncertainty quantification is a multi-disciplinary field which is an inseparable part of risk analysis. However, many real-world problems deal with large number of simulations or experiments. Considering the limited budget and time to perform all these efforts (specially for practitioners), an essential task is to reduce the computational cost in an uncertain environment.This paper proposes to use a matrix completion technique for reducing the overall computational cost of engineering systems when they are subjected to the simultaneous effects of aleatory and epistemic uncertainties with high dimensions. The proposed method is further improved using hidden information in the uncertain variables based on clustering techniques. Several parametric and Monte Carlo simulations were performed to demonstrate the accuracy of our method with different compression ratios. Experimental results show a decent overall performance of our technique for high-dimensional hybrid uncertain systems.     

Robust digital controllers for uncertain chaotic systems: A digital redesign approach

In this paper, a new and systematic method for designing robust digital controllers for uncertain nonlinear systems with structured uncertainties is presented. In the proposed method, a controller is designed in terms of the optimal linear model representation of the nominal system around each operating point of the trajectory, while the uncertainties are decomposed such that the uncertain nonlinear system can be rewritten as a set of local linear models with disturbed inputs. Applying conventional robust control techniques, continuous-time robust controllers are first designed to eliminate the effects of the uncertainties on the underlying system. Then, a robust digital controller is obtained as the result of a digital redesign of the designed continuous-time robust controller using the state-matching technique. The effectiveness of the proposed controller design method is illustrated through some numerical examples on complex nonlinear systems––chaotic systems.     

On the robustness of linear stabilizing feedback control for linear uncertain systems: Multi-input case

The robust stabilization of linear systems with constant uncertainties against structured perturbations using Lyapunov's theory is investigated. The only information needed on the uncertainties is the knowledge of their boundaries. The matching conditions of the uncertain systems are not required to be satisfied. It is first shown that, under some assumptions, the system can be transformed into a certain canonical controllable companion form. Then, under some additional assumptions, the existence of a linear controller which stabilizes the system based on Lyapunov's theory is shown.     

On the rate allocation problem under co-existence of uncertain utility function parameters and uncertain link capacities

The allocation problem is considered for a computer communication network with network links being shared among multiple transmissions running at the same time. Typically, each transmission brings benefit to a network owner proportional to the transmission rate allowed. Since the link capacities are limited and the minimal rates defined by Quality of Service (QoS) requirements have to be preserved, the rate allocation problem is defined here as a problem of maximizing the overall benefit (utility) under capacity and QoS constraints. In this paper, we consider the uncertain version of the problem, in which unknown utility function parameters and unknown link capacities are treated as uncertain variables characterized by certainty distributions. We present a method for solving the problem in a unified way, with both uncertainties combined in a single certainty index.     

Heterogeneous uncertainties in cholesterol management

Physicians use clinical guidelines to inform judgment about therapy. Clinical guidelines do not address three important uncertainties: (1) uncertain relevance of tested populations to the individual patient, (2) the patient’s uncertain preferences among possible outcomes, and (3) uncertain subjective and financial costs of intervention. Unreliable probabilistic information is available for some of these uncertainties; no probabilities are available for others. The uncertainties are in the values of parameters and in the shapes of functions. We explore the usefulness of info-gap decision theory in patient-physician decision making in managing cholesterol level using clinical guidelines. Info-gap models of uncertainty provide versatile tools for quantifying diverse uncertainties. Info-gap theory provides two decision functions for evaluating alternative therapies. The robustness function assesses the confidence—in light of uncertainties—in attaining acceptable outcomes. The opportuneness function assesses the potential for better-than-anticipated outcomes. Both functions assist in forming preferences among alternatives. Hypothetical case studies demonstrate that decisions using the guidelines and based on best estimates of the expected utility are sometimes, but not always, consistent with robustness and opportuneness analyses. The info-gap analysis provides guidance when judgment suggests that a deviation from the guidelines would be productive. Finally, analysis of uncertainty can help resolve ambiguous situations.