基于关系代数的软集合理论研究与应用

基于关系代数理论中的部分思想,定义了软集合理论中的差运算、选择运算和投影运算.探讨了关系代数和软集合的关系,运用关系代数的选择、投影、并、差等运算实现了软集合参数约简算法,并用SQL语言实现了算法.最后将算法运用到房屋置业选择问题中进行验证.结果表明,软集合方法能以一种更简单直接的形式为决策问题提供有效的参考依据.     

基于模糊双射软集合的内河岸线资源KDD模型

内河岸线资源评价与分析方法主要是针对定量数据,而一些影响岸线资源价值的因素是定性的、模糊的,传统方法在处理此类问题时存在局限。针对该问题,本文运用近年来国际上新兴的不确定问题建模理论——软集合理论,考虑模糊环境,结合基于依赖度的模糊双射软集合参数约减方法,提出了一种基于模糊双射软集合的内河岸线资源评价KDD模型。本文采用重庆港区15个港口进行KDD分析。结果表明本模型能够提取决策规则、分析关键因素及其依赖度,并得到了有参考价值的决策建议,验证了该模型的有效性。     

不完备模糊目标信息系统的α,β精度约简算法

本文首先定义了不完备模糊目标信息系统及其非对称相似关系,然后借鉴经典的可辨识矩阵精度约简算法,提出一种新的基于非对称相似关系的可辨识矩阵(α,β)精度约简算法,对不完备模糊目标信息系统进行属性约简.最后给出一个实例,检验算法的可行性.     

求有限集合覆盖的构造方法

在近似算法领域,集合覆盖问题是研究的比较早和比较透彻的问题之一.文中解决与经典SCP不同的另一问题,针对有限集合覆盖的构造,提出一种构造有限集合上的集合覆盖的算法,并且给出了该算法的完备性证明.该算法简单有效,是一种用于构造集合覆盖的规范方法.     

涉及相变问题的Julia集

考虑反铁磁链对应的金刚石型等级晶格上的Potts模型, 研究复相变点集的性质. 这些集合是一族有理映照的Julia集, 证明了它们可能是不连通的集合, 并就其拓扑结构给出了比较完备的刻画.     

基于粗糙集的突发事件属性约简方法

针对突发事件不完备信息系统中的原始数据存在大量属性冗余的问题,提出一种基于粗糙集的不完备信息系统属性约简方法,以剔除冗余属性,提高知识清晰度。首先对缺失、冗余、噪声以及连续型数据进行预处理;然后进行属性分类,将属性分为条件属性与决策属性,进而建立决策表;最后根据决策表的特征,结合有序加权平均算子的思想,提出一种基于属性重要度的启发式属性约简算法。文末,通过实例验证了方法的正确性与有效性,并利用该方法实现了火灾数据的属性约简。     

不完备信息系统中近似算子研究

近年来出现了许多用粗糙集理论处理各种信息系统的方法,其中对不完备信息系统的研究一直是个热点。本文采用Grzymala-Busse提出的刻画关系研究"丢失"和"暂缺"两种语意并存的不完备信息系统,考察了点近似、子集近似和概念近似算子的性质,并且给出这些上、下近似之间关于包含关系的序关系,为进一步研究不完备信息系统打下理论基础。     

涉及Yang-Lee理论的Fatou集

考虑金刚石型等级晶格上的Potts模型, 研究了复相位对应点集的性质. 这些集合事实上是一族有理映射的Fatou集. 就其拓扑结构给出了比较完备的刻画.     

不完备多尺度决策信息系统的局部泛化约简

在不完备多尺度决策信息系统中,研究面向单个对象的局部泛化约简方法。所提的局部泛化约简方法融合了不同特征的不同尺度上的信息,且保持了广义决策值的一致性。在协调的不完备多尺度决策信息系统中,证明了两种泛化约简方法的一致性,给出了相应的的局部泛化约简算法。在不协调多尺度决策信息系统中,研究了其详细性质,并通过实例说明两种泛化约简的区别。     

不完备区间值信息系统中基于相容-相似关系的模糊粗糙集方法

区间值信息系统是单值信息系统的推广模型.区间值是描述影响观测对象值的一种表示方法.然而,在实际应用中,由于遗漏、测量误差、数据传输、介质噪声等多种因素的影响,区间值往往会出现缺失值.如果忽略或丢弃包含缺失值的数据,则必须丢弃大量信息.主要针对不完备区间值信息系统,提出一种基于相容-相似关系的模糊粗糙集方法.在此基础上,推广了模糊近似算子的定义,研究了不确定性度量问题.     

Bijective soft set decision system based parameters reduction under fuzzy environments

Gong et al. (2010) and Xiao et al. (2010) have proposed the notion of bijective soft set and exclusive disjunctive soft set, respectively, which is a subtype of soft set. On the basis of their work, this paper extends these notions to fuzzy environments, and formulates the concept of bijective fuzzy soft set, which can deal with more uncertain problems. Moreover, this paper proposes two parameters reduction algorithms: one (Algorithm 1) is based on bijective fuzzy soft system, and the other (Algorithm 2) takes weight of an element into consideration. Since the threshold plays an important role in these algorithms, we proposed an algorithm (Algorithm 3) to decide the optimal value of threshold specially. Afterwards, an example analysis of the two parameters reduction algorithms is given and the result shows that the two algorithms lead to the same parameters reduction of a bijective fuzzy soft system. Since Algorithm 2 considers the detail weights of elements, thus it can be used in more uncertain problems, such as time series analysis problems, than Algorithm 1.     

多方参与且评价指标集有差异的方案筛选方法

针对多方给出评价指标集存在差异情形的方案筛选问题,提出了一种基于软集理论的方案筛选方法。在该方法中,首先依据各方给出关于备选方案符合评价指标要求的可接受水平,构建各方相应的评价指标子集,进而针对各备选方案符合评价指标要求的结果构建针对各方评价指标子集的软集;然后构建所有方案均符合全部指标要求的软集,进而通过各评价指标子集的软集与所有方案均符合全部指标要求的软集的积运算和算子运算,得到对应评价指标子集的每方可接受的备选方案集;进一步地,通过各方可接受的备选方案集间的并集运算及交集运算,可得到最终的方案筛选结果。最后,通过一个算例说明了本文给出方法的可行性和有效性。     

The trapezoidal fuzzy soft set and its application in MCDM

Soft set theory is a newly emerging mathematical tool to deal with uncertain problems. Since the trapezoidal fuzzy number, as a vital concept of fuzzy set, can express linguistic assessments by transforming them into numerical variables objectively, this paper aims to extend classical soft sets to trapezoidal fuzzy soft sets based on trapezoidal fuzzy numbers. Then, some operations on a trapezoidal fuzzy soft set are defined, such as complement operation, “AND” operation, and “OR” operation. Finally, a Multiple Criterion Decision-Making (MCDM) problem under a fuzzy environment is analyzed by trapezoidal fuzzy soft sets with the demonstration of a numerical example. This paper also uses traditional fuzzy soft sets to deal with the MCDM problem. The result shows that the method provided by this paper outperforms the traditional one.     

An adjustable approach to fuzzy soft set based decision making

Molodtsov’s soft set theory was originally proposed as a general mathematical tool for dealing with uncertainty. Recently, decision making based on (fuzzy) soft sets has found paramount importance. This paper aims to give deeper insights into decision making based on fuzzy soft sets. We discuss the validity of the Roy-Maji method and show its true limitations. We point out that the choice value designed for the crisp case is no longer fit to solve decision making problems involving fuzzy soft sets. By means of level soft sets, we present an adjustable approach to fuzzy soft set based decision making and give some illustrative examples. Moreover, the weighted fuzzy soft set is introduced and its application to decision making is also investigated.     

A novel approach to interval-valued 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. The interval-valued intuitionistic fuzzy soft set is a combination of an interval-valued intuitionistic fuzzy set and a soft set. The aim of this paper is to investigate the decision making based on interval-valued intuitionistic fuzzy soft sets. By means of level soft sets, we develop an adjustable approach to interval-valued intuitionistic fuzzy soft sets based decision making and some numerical examples are provided to illustrate the developed approach. Furthermore, we also define the concept of the weighted interval-valued intuitionistic fuzzy soft set and apply it to decision making.     

An adjustable approach to intuitionistic fuzzy soft sets based decision making

Molodtsov initiated the concept of soft set theory, which can be used as a generic mathematical tool for dealing with uncertainty. There has been some progress concerning practical applications of soft set theory, especially the use of soft sets in decision making. In this paper we generalize the adjustable approach to fuzzy soft sets based decision making. Concretely, we present an adjustable approach to intuitionistic fuzzy soft sets based decision making by using level soft sets of intuitionistic fuzzy soft sets and give some illustrative examples. The properties of level soft sets are presented and discussed. Moreover, we also introduce the weighted intuitionistic fuzzy soft sets and investigate its application to decision making.     

Some results on soft element and soft topological space

All over the globe, soft set theory is a topic of interest for many authors working in diverse areas because of its rich potential for applications in several directions since the day it was defined by Molodtsov in 1999. Moreover, soft set theory is free from the difficulties where as other existing methods viz. probability theory, fuzzy set theory. Considering to these benefits, soft set theory has became very popular research area for many researchers. To contribute this research area, in this paper, we examine some properties on soft topological spaces such as neighborhood structure of a soft element and soft interior, soft closure, and soft cluster element and so on that are based on soft element definition that gives us a different perspective for development of soft set theory. Moreover, we give some examples to clarify our definitions.