模糊Jena的研究

用Jena[11]建立的本体中概念及其关系都是确定的,但是,现实世界中事物及其关系往往具有模糊性且这种模糊性会随着事物的发展而变化.为了将Web上资源及其关系的模糊性表示出来,在RDF[9]的三元组(主体、谓词、客体)中的谓词部分加入一个表示谓词的模糊性的权值,提出一个新的概念-模糊资源描述框架(FRDF,Fuzzy ...     

一种支持模糊QoS属性的Web服务发现算法

目前的服务发现方法都是假设服务的QoS属性以一个精确的实数来表示,并未考虑QoS属性的模糊性,由于某些QoS属性的模糊性,用一个精确的实数来描述其值是不合理的。运用区间数的方式来描述那些带有模糊性的QoS属性,在此基础上提出了一种支持模糊QoS属性的服务发现算法,实验验证了该算法的可行性。     

当代艺术审美观照下的模糊美感知

艺术中的难题归结于有限与无限的矛盾,而艺术本身就是个无限接近的矛盾。人们对美的多维的、立体的认识的模糊性在于美的内部结构层次的复杂性,是以主观意志而转移的动态系统。如同美与丑的模糊属性,事物在美丑之间连续分布无法作为客体存在,绝对的差异界限并不科学。美学领域因模糊体系的观照逐渐成为了充满多样性和可能性的科学系统。     

一个带有相似性关系的模糊逻辑

模糊集与模糊逻辑是处理模糊性与不确定性信息的重要数学工具,相似性关系是模糊集的一个基本概念。为了在模糊逻辑中集成相似性关系并考虑其模糊推理,提出了一个带有相似性关系的模糊逻辑,给出了其语法及语义描述,在模糊谓词逻辑情形下,讨论并证明了基于归结与调解方法的模糊推理的有关属性,考虑到许多定理证明器和问题解决系统均是基于否证法,证明了归结与调解方法对模糊谓词演算的反驳完备性定理。     

模糊集方法在空间数据处理中的应用综述

自然界中存在着大量模糊现象，地理对象定义中也存在模糊性。传统空间数据处理中将现实世界当作确定的对象来处理，几乎不能处理模糊现象，从而限制了GIS等空间数据处理技术的进一步发展。通常，用模糊集理论来解决数据的模糊性问题。本介绍了模糊集的概念及基本原理和模糊地理对象及模糊拓扑关系的研究进展，并且从土地适宜性评价，专题地图各专题要素的面积估算，遥感图像分类信息提取，地理数据库的设计，模糊地理对象建模以及模糊集与其它软计算方法的结合等六个方面概述了模糊集理论在空间数据处理应用中的进展。     

骨髓细胞显微图像的分类认知方法研究

骨髓细胞分类识别过程中,对细胞形态属性识别的模糊性以及各属性与细胞类判定之间关系的模糊性,极大影响了细胞类的正确识别,致使目前对细胞图像分析处理仅能进行到六类细胞类粗略划分。结合模糊理论的隶属度定义及综合有权评判思想,给出了隶属函数及权重的确立方法,进而提出了基于专家知识的隶属度模糊识别机制,大大增加了骨髓细胞可识别类的细分程度。并在不影响识别效果的条件下,结合决策树判别,提高了识别的效率。     

基于模糊不一致对的多标记属性约简

在实际生活当中,存在着大量的高维多标记数据,为解决维度灾难问题,通常需要约简属性集。针对目前的多标记属性约简算法未考虑标记关系问题,本文提出了一种融合标记关系的模糊不一致对多标记属性约简算法。利用相对熵(KL散度)度量标记之间的关系,定义标记权重,结合标记权重,定义模糊不一致对,考虑到属性对于模糊不一致对的区分性,定义属性重要性并进行属性约简。在8个数据集上的对比实验表明,所提基于模糊不一致对的多标记属性约简算法优于当前的多标记属性约简算法。     

自组织神经网络在模糊聚类中的应用研究

聚类是按照事物的某些属性,把事物分类,使类间的相似性尽量小,类内的相似性尽量大.将事物通过适当聚类,才能便于研究事物的内部规律,但客观世界中存在着大量界线不分明的问题,研究模糊聚类的方法正是为了解决这类问题.在对常规模糊聚类方法分析的基础上,提出了一种将自组织竞争神经网络技术运用于模糊聚类的一种方法,并以100种动物分类为例,进行了模拟试验,仿真结果证明这种方法进行模糊聚类的思想正确,方法可行,效果较好.     

模糊粗糙集的鱼病规则获取方法

针对鱼病诊断中存在随机性、诊断信息的模糊性和信息获取的不完备性等不确定因素。首先给出模糊的鱼病诊断过程,由于该方法存在模糊诊断矩阵较难获取的缺点。利用粗糙集理论无需先验知识的优点,将粗糙集理论和模糊集结合用于鱼病的诊断中。鱼病的症状模糊集形成条件属性,疾病模糊集形成决策属性构成模糊信息表,给出了模糊信息表属性约简和鱼病诊断规则获取的算法步骤,最后以实例演示了鱼病诊断的规则获取过程。论文为鱼病的诊断提供了一种全新的方法。     

基于模糊数学理论的信誉评估算法研究

针对信任评估的主观性、模糊性以及无法精确地描述和验证等问题,提出信誉评估算法.算法定义了信任度、隶属度、信任关系、关键属性以及模糊变换等基本概念和关键属性集U、代理集Y、评价集V、评价集矩阵R四个要素.根据模糊数学理论,将代理X的信任程度以及各代理对被代理的各关键属性的评估进行模糊变换,得到即将交易的代理Y的信任向量,并根据实际对原代理X的各信任值向量化,最终得到对代理X的信任值或信誉.实例表明,算法能用直观、简洁的语义定量地描述信任值,反映了实体之间信任的模糊性,解决了关于模糊性实体之间的信任建模问题,适用于电子商务、电子政务等领域.     

Handling fuzzy information in extended possibility‐based fuzzy relational databases

Two kinds of fuzziness in attribute values of the fuzzy relational databases can be distinguished: One is that attribute values are possibility distributions, and the other is that there are resemblance relations in attribute domains. The fuzzy relational databases containing these two kinds of fuzziness simultaneously are called extended possibility‐based fuzzy relational databases. In this paper, we focus on such fuzzy relational databases. We classify two kinds of fuzzy data redundancies and define their removal. On this basis, we define fuzzy relational operations in relational algebra, which, being similar to the conventional relational databases, are complete and sound. In particular, we investigate fuzzy querying strategies and give the form of fuzzy querying with SQL. © 2002 Wiley Periodicals, Inc.     

Updating extended possibility‐based fuzzy relational databases

Two kinds of fuzziness in attribute values of the fuzzy relational databases can be distinguished: one is that attribute values are possibility distributions and the other is that there are resemblance relations in attribute domains. The fuzzy relational databases containing these two kinds of fuzziness simultaneously are called extended possibility‐based fuzzy relational databases. In this article, we focus on such fuzzy relational databases and investigate three update operations for the fuzzy relational databases, which are Insertion, Deletion, and Modification, respectively. We develop the strategies and implementation algorithms of these operations. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 237–258, 2007.     

Fuzzy relational algebra for possibility-distribution-fuzzy-relational model of fuzzy data

In the real world, there exist a lot of fuzzy data which cannot or need not be precisely defined. We distinguish two types of fuzziness: one in an attribute value itself and the other in an association of them. For such fuzzy data, we propose a possibility-distribution-fuzzy-relational model, in which fuzzy data are represented by fuzzy relations whose grades of membership and attribute values are possibility distributions. In this model, the former fuzziness is represented by a possibility distribution and the latter by a grade of membership. Relational algebra for the ordinary relational database as defined by Codd includes the traditional set operations and the special relational operations. These operations are classified into the primitive operations, namely, union, difference, extended Cartesian product, selection and projection, and the additional operations, namely, intersection, join, and division. We define the relational algebra for the possibility-distribution-fuzzy-relational model of fuzzy databases.     

Data dependencies in extended possibility‐based fuzzy relational databases

Based on the semantic equivalence degree the formal definitions of fuzzy functional dependencies (FFDs) and fuzzy multivalued dependencies (FMVDs) are first introduced to the fuzzy relational databases, where fuzziness of data appears in attribute values in the form of possibility attributions, as well as resemblance relations in attribute domain elements, called extended possibility‐based fuzzy relational databases. A set of inference rules for FFDs and FMVDs is then proposed. It is shown that FFDs and FMVDs are consistent and the inference rules are sound and complete, just as Armstrong's axioms for classic cases. © 2002 Wiley Periodicals, Inc.     

Semantic measure of fuzzy data in extended possibility‐based fuzzy relational databases

In this paper, we propose notions of equivalence and inclusion of fuzzy data in relational databases for measuring their semantic relationship. The fuzziness of data appears in attribute values in forms of possibility distribution as well as resemblance relations in attribute domain elements. An approach for evaluating semantic measures is presented. With the proposal, one can remove fuzzy data redundancy and define fuzzy functional dependency. © 2000 John Wiley & Sons, Inc.     

模糊时态数据库关系代数演算规则分析

为了解决时态数据库对客观世界更符合实际抽象描述,能有效表示和处理复杂对象的模糊时态特性,从基本模糊时态数据模型入手,定义了一系列模糊时态关系操作元素,并论证了模糊时态区间关系代数的语法和语义,建立了一个能描述模糊时态信息的关系代数理论体系.避免了时态数据库不能提供表达模糊时态信息的环境设施,也增强了现有的时态数据库描述模糊事物特性的能力.     

基于熵和相关系数的直觉模糊多属性决策方法

针对决策信息为直觉模糊信息且属性权重完全未知的多属性决策问题,提出了一种基于直觉模糊熵和得分函数的决策方法。为了准确度量直觉模糊集的直觉性和模糊性,首先提出了一种新的直觉模糊熵,并讨论其相关性质。其次为了减少不确定信息对决策的影响,结合直觉模糊熵建立规划模型,从而确定属性权重。 同时从隶属度,非隶属度和犹豫度三方面构建论域对象与理想对象之间相关系数,并在此基础上根据决策者的决策态度定义得分函数进而得到最优决策。 最后给出一种基于直觉模糊信息的多属性决策方法,并通过候选人评估实例验证了该方法的可行性和有效性。     

基于Vague集的时态数据库

为了更加精确地表示模糊信息,数据库的模糊属性采用Vague集来表示.基于Vague集的数据库模型与现有模糊关系数据模型相比,有更强的模糊表达能力,数据库中的每一数据及元组均可分别从真、假隶属度及未知程度三个侧面予以体现.在Vague数据库模型的基础上进行了扩展,提出了基于Vague集的时态数据库,为进一步研究Vague时态数据库的数据模型、数据理论等相关问题打下了基础.     

GRA method for multiple attribute decision making with incomplete weight information in intuitionistic fuzzy setting

The aim of this paper is to investigate the multiple attribute decision-making problems with intuitionistic fuzzy information, in which the information about attribute weights is incompletely known, and the attribute values take the form of intuitionistic fuzzy numbers. In order to get the weight vector of the attribute, we establish an optimization model based on the basic ideal of traditional grey relational analysis (GRA) method, by which the attribute weights can be determined. Then, based on the traditional GRA method, calculation steps for solving intuitionistic fuzzy multiple attribute decision-making problems with incompletely known weight information are given. The degree of grey relation between every alternative and positive-ideal solution and negative-ideal solution are calculated. Then, a relative relational degree is defined to determine the ranking order of all alternatives by calculating the degree of grey relation to both the positive-ideal solution (PIS) and negative-ideal solution (NIS) simultaneously. Finally, an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness.     

面向语义信息查询的模糊本体模型

针对领域知识建模时的模糊性、不确定性与信息查询时的局限性,提出一种基于模糊控制规则的模糊本体模型。利用基于模糊聚类的本体机器学习方法构建模糊控制规则库,通过计算模糊相似矩阵得到模糊概念的语义关联,对词汇相关概念进行语义分析与扩展获取模糊概念间的本质语义关系,实现基于模糊概念属性值的信息查询与语义共用。实验结果表明,该模型在语义查询上有更完善的推理机制,能有效获取语义信息。