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。     

基于合理粒度原则的多层模糊规则模型构建

模糊IF-THEN规则模型因其可产生具有较好解释性的推理结果受到了广泛的关注。对于高维、复杂的问题,模糊IF-THEN规则模型却未充分利用数据特征中包含的层次信息,对数据的多水平表征能力较弱。此外,模糊模型的构建往往受到数据质量、专家知识等因素的影响导致数值输出伴随着不确定性。本文基于合理粒度原则提出了一种具有多层结构的Takagi-Sugeno(T-S)模糊模型的建模方法。该方法在粒计算框架下将模糊技术与多层学习策略相结合,并在数据子空间内部采取逐层划分的方法进一步挖掘数据中隐含的结构信息,使模型具有良好的可解释性,同时以信息粒为输出体现了主要的预测范围。最后,在公开数据集上进行数据实验,检验了所提方法的有效性。     

基于云重心法评估复杂电磁环境下炮兵信息化作战能力

炮兵信息化作战能力评估是一个复杂的多指标综合体系,包含许多随机和模糊因素.特别是在复杂的电磁环境下,对炮兵的信息系统和信息化作战能力影响巨大,更增加了炮兵信息化作战能力的不确定性.结合炮兵信息化作战能力评估模型,为能在建模分析过程中更好的处理较多的模糊指标,所以运用"云重心"理论确定了评估模型中各指标基本评定值,增强了模型的可信度和可操作性.较准确的反映了炮兵在复杂的电磁环境下信息化作战能力,为以后评估炮兵在复杂的电磁环境下信息化作战能力提供了准确的依据和方法.     

概率型数据模糊化方法研究

在实际中用一个精确量很难描述一个比较模糊的概念，如好坏、高低等，而用模糊数学方法往往能很好地解决这类问题。在模糊评估和模糊控制中，常常碰到隶属度如何确定或将精确数据如何模糊化的问题。本文采用正态分布模型对概率型数据进行模糊化，并针对正态分布模型存在边缘非单调性的缺陷，对模型进行了改进，最终建立了改进型正态分布模糊化模型，通过模糊化实例计算，证明这种方法是有效可行的。     

基于模糊证据推理的汽车制造产业网络环境复杂性测度

环境复杂性是汽车制造产业网络复杂性的主要诱因。利用指标波动程度测度复杂性的思想,建立表征汽车制造产业网络外部环境的指标体系,构建指标变动程度的模糊信度结构模型,运用模糊证据推理算法结合熵值理论对多指标进行信息融合,并利用灰色关联分析方法评估复杂性测度结果。通过2002~2012年汽车行业数据的实证研究,结果表明:经济环境与国际环境的波动是导致汽车制造产业网络复杂性的最主要因素,技术环境和市场环境次之;近10年来我国汽车制造产业网络外部环境经历了两次较大波动,总体复杂性呈现增长趋势。本研究可为汽车产业环境复杂性测度及应对提供思路参考。     

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。     

基于随机模糊理论的结构可靠性分析

在结构可靠性分析中，由于缺乏足够数据或信息不完整，元件强度和外载的分布参数只能根据已有的实验和专家的经验采用模糊变量进行描述。此时强度和外载是随机模糊变量，基于随机模糊理论，本文提出概率模型的分布参数为模糊变量时的结构可靠性度量指标计算模型。该模型不但可以解决应力和强度服从任意分布时的可靠度问题而且也为计算多变量模型提供了一种计算可靠度的方法。最后通过算例，验证了该方法的有效性和合理性。     

改进模糊层次分析法在初级任职教育院校教学工作评价中的应用

针对初级任职教育院校教学工作评价指标多,体系复杂,难以完全量化评估的特点,引入改进的模糊层次分析法,为指标建立权重,并用相容性指标进行矩阵一致性检验,构建了模糊评价模型,提出了初级任职教育院校教学工作评价的多目标决策问题解决方案。该方法简便可行,可靠性好,与其它评价方法相比更加客观准确。     

基于合成规则的相似性推理及其在焊接参数设计中的应用

焊接过程是典型的复杂过程,应用模糊逻辑建立焊接参数推理系统可快速实现焊接工艺参数设计.近似推理机制是模糊系统研究和开发中的一个重要组成部分,在现有的相似性推理方法的基础上,融合经典的合成规则推理技术,提出一种新的基于合成规则的相似性推理方法.以CO_2保护焊焊接规范参数设计为背景介绍模糊推理在焊接领域的应用,运行结果表明,采用方法建立的模糊推理模型具有较高的推算精度,能够满足焊接工艺参数设计的要求.     

态势估计中一种模糊时间推理方法

时间推理是态势估计的一个重要组成部分,本文研究了当两个模糊时间点之间的时间间隔已知时,综合来自不同推理路径的推理结果方法,并建立了检验时间数据与专家知识模型符合程度的方法及时间网络的一致性检验方法。     

三角模糊数去模糊化对VIKOR妥协解的影响研究

针对准则值和准则权重均为三角模糊数的多准则决策问题,研究了不同三角模糊数去模糊化方法适用的数学运算规则,应用VIKOR方法进行三角模糊数去模糊化的必要环节和前提条件,分析了去模糊化对群体效用值和个体遗憾值以及对妥协解的影响机制,给出了一种拓展的VIKOR方法的决策步骤,最后运用算例说明了方法的实施过程和有效性。     

Graph-based multi-agent decision making

In lots of practical multi-criteria decision making (MCDM) problems, there exist various and changeable relations among the criteria which cannot be handled well by means of the existing methods. Considering that graphic or netlike structures can be used to describe the relationships among several individuals, we first introduce the graphic structure into MCDM and formalize the relations among criteria. Then, we develop a new tool, called graph-based multi-agent decision making (GMADM) model, to deal with a kind of MCDM problems with the interrelated criteria. In the model, the graphic structure is paid sufficient attention to in two main aspects: (1) how the graphic structure has influence on the benefits of agents (or the criteria values); and (2) the relation between the graphic structure and the importance weights of agents (criteria). In this case, we can select the best plan(s) (or alternative(s)) according to the overall benefits (the overall criteria values) resulting from the model. Moreover, a fuzzy graph-based multi-agent decision making (FGMADM) method is developed to solve a common kind of situations where the graphic structure of agents is uncertain (confidential or false). Three examples are used to illustrate the feasibility of these two developed methods.     

Multi-criteria analysis for dredger dispatching under uncertainty

Dredger dispatching is a complex decision making process in which multiple requirements and uncertain site conditions have to be taken into consideration simultaneously, for a specific dredging task. In evaluating the suitability of dredgers, besides quantitative assessments, qualitative assessments are often required to deal with uncertainty, subjectiveness and imprecision, which are best represented with fuzzy data. This paper formulates dredger dispatching as a fuzzy multi-criteria analysis model, and presents an effective algorithm for handling both crisp and fuzzy data in a straightforward manner. As a result, effective decisions can be made based on consistent evaluation results. An empirical study of a real case in China is conducted to demonstrate the applicability of the model. With its simplicity in both concept and computation, the model can be implemented as an effective decision aid in selecting dredgers for specific dredging tasks.     

A confidence voting process for ranking problems based on support vector machines

In this paper, we deal with ranking problems arising from various data mining applications where the major task is to train a rank-prediction model to assign every instance a rank. We first discuss the merits and potential disadvantages of two existing popular approaches for ranking problems: the ‘Max-Wins’ voting process based on multi-class support vector machines (SVMs) and the model based on multi-criteria decision making. We then propose a confidence voting process for ranking problems based on SVMs, which can be viewed as a combination of the SVM approach and the multi-criteria decision making model. Promising numerical experiments based on the new model are reported. The research of the last author was supported by the grant #R.PG 0048923 of NESERC, the MITACS project “New Interior Point Methods and Software for Convex Conic-Linear Optimization and Their Application to Solve VLSI Circuit Layout Problems” and the Canada Researcher Chair Program.     

Research on AHP with interval-valued intuitionistic fuzzy sets and its application in multi-criteria decision making problems

This paper investigates an approach for multi-criterion decision making (MCDM) problems with interval-valued intuitionistic fuzzy preference relations (IVIFPRs). Based on the novel interval score function, some extended concepts associated with IVIFPRs are defined, including the score matrix, the approximate optimal transfer matrix and the possibility degree matrix. By using these new matrixes, a prioritization method for IVIFPRs is proposed. Then, we investigate an interval-valued intuitionistic fuzzy AHP method for multi-criteria decision making (MCDM) problems. In the end, a numerical example is provided to illustrate the application of the proposed approach.     

不完全语言信息下的多准则群决策方法研究

针对决策者所给的自然语言信息缺失判断矩阵,提出了一种基于群体满意度最大的不完全语言信息多准则群决策规划模型.首先分析决策者所给的多准则语言评价信息矩阵,进而通过三角模糊数将多准则语言评价信息矩阵转化为三角模糊数矩阵;其次根据满意度函数构建不完全语言信息多准则群决策规划模型;最后通过实例验证本方法的可行性及有效性.实例表明该方法计算简单,易操作.     

Solving the multiobjective possibilistic linear programming problem

In conventional multiobjective decision making problems, the estimation of the parameters of the model is often a problematic task. Normally they are either given by the decision maker (DM), who has imprecise information and/or expresses his considerations subjectively, or by statistical inference from past data and their stability is doubtful. Therefore, it is reasonable to construct a model reflecting imprecise data or ambiguity in terms of fuzzy sets for which a lot of fuzzy approaches to multiobjective programming have been developed. In this paper we propose a method to solve a multiobjective linear programming problem involving fuzzy parameters (FP-MOLP), whose possibility distributions are given by fuzzy numbers, estimated from the information provided by the DM. As the parameters, intervening in the model, are fuzzy the solutions will be also fuzzy. We propose a new Pareto Optimal Solution concept for fuzzy multiobjective programming problems. It is based on the extension principle and the joint possibility distribution of the fuzzy parameters of the problem. The method relies on α-cuts of the fuzzy solution to generate its possibility distributions. These ideas are illustrated with a numerical example.     

Maximin Programming,Fuzzy Linear Programming and Multi-Criteria Decision Making

Mathematical programming models present an approach to solving multicriteria decision problems. This paper compares two such models. One based on fuzzy set theory, the other relying on utility functions and the maximin decision criteria. The two models have identical form. The paper also illustrates the relationship between fuzzy set theory and multi-criteria decision making.     

A rough set approach to intuitionistic fuzzy soft set based decision making

The soft set theory, originally proposed by Molodtsov, can be used as a general mathematical tool for dealing with uncertainty. Since its appearance, there has been some progress concerning practical applications of soft set theory, especially the use of soft sets in decision making. The intuitionistic fuzzy soft set is a combination of an intuitionistic fuzzy set and a soft set. The rough set theory is a powerful tool for dealing with uncertainty, granuality and incompleteness of knowledge in information systems. Using rough set theory, this paper proposes a novel approach to intuitionistic fuzzy soft set based decision making problems. Firstly, by employing an intuitionistic fuzzy relation and a threshold value pair, we define a new rough set model and examine some fundamental properties of this rough set model. Then the concepts of approximate precision and rough degree are given and some basic properties are discussed. Furthermore, we investigate the relationship between intuitionistic fuzzy soft sets and intuitionistic fuzzy relations and present a rough set approach to intuitionistic fuzzy soft set based decision making. Finally, an illustrative example is employed to show the validity of this rough set approach in intuitionistic fuzzy soft set based decision making problems.