Clinical Pure Types as a Fuzzy Partition

Abstract

This paper discusses the applicability of the ideas of fuzzy sets and grades of membership to problems encountered in the quantification of clinical (i.e., diagnostic and prognostic) judgment. The methods of constrained maximum likelihood are used to derive consensus estimates of grades of membership given a set of categorical data and an a priori set of specified pure types. A numerical example is given.     

COMPUTATIONAL VERB SYSTEMS: VERB SETS

Abstract

A computational verb set (verb set for short) consists of a computational verb and a crisp set or a fuzzy set. Verb sets generalize the other sets from linguistic structure ”BE + statement” to “verb + statement”. Verb sets are strongly connected to computational verbs and their contexts. In this paper, the framework of verb sets and some basic concepts and properties of verb sets are given. An application of verb sets to chaotic cryptanalysis is presented to illustrate how verb sets are constructed in real-life applications.     

GROUP DECISION-MAKING WITH A FUZZY MAJORITY VIA LINGUISTIC QUANTIFIERS. PART II: A COMPETITIVE-LIKE POOLING

Abstract

Starting from individual fuzzy preference relations, some (sets of) socially best acceptable options are determined, directly or via a social fuzzy preference relation. An assumed fuzzy majority rule is given by a fuzzy linguistic quantifier, e.g., “most.” Here, as opposed to Part I, where we used a consensory-like pooling of individual opinions, we use an approach to linguistic quantifiers that leads to a competitive-like pooling. Some solution concepts are considered: cores, minimax (opposition) sets, consensus winners, and so forth,     

基于动态双极值模糊软集的群决策方法

针对实际问题中双极值模糊软集随时间变化的影响，定义了动态双极值模糊软集等概念，讨论了相关运算及性质。根据时间权重符合对数增长模型得到权重确定公式。利用集成思想定义双极值模糊软集的运算并给出几何加权平均算子的计算公式，将动态双极值模糊软集集成为综合双极值模糊软集。利用水平软集算出各对象的机会值，得出最优决策。通过实例分析证明此决策方法的合理性与可行性。     

On the representations of L-equivalence relations on L-fuzzy sets with applications to locally vague environments

ABSTRACT

L-equivalence relations on L-fuzzy sets, equivalent to L-valued equalities on ordinary sets, are modelling fuzzy similarity of two objects that are partial members of a fuzzily defined set. This paper deals with construction and representation properties of L-equivalence relations on L-fuzzy sets. To this end, we set up a method for constructing them by means of quasi-pseudo-metrics, and then point out how L-equivalence relations on L-fuzzy sets are represented by families of L-fuzzy subsets of L-fuzzy sets. The present results are applied to locally vague environments to elucidate conceptual structures of locally vague environments, and to show how the points in a locally vague environment are described.     

The Representation of Fuzzy Knowledge

Abstract

A new AI programming language (called FUZZY) is introduced which provides a number of facilities for efficiently representing and manipulating fuzzy knowledge. A fuzzy associative net is maintained by the system, and procedures with associated “procedure demons” may be defined for the control of fuzzy processes. Such standard AI language features as a pattern-directed data access and procedure invocation mechanism and a backtrack control structure are also available.

This paper examines some general techniques for representing fuzzy knowledge in FUZZY, including the use of the associative net for the explicit representation of fuzzy sets and fuzzy relations, and the use of “deduce procedures” to implicitly define fuzzy sets, logical combinations of fuzzy sets, linguistic hedges, and fuzzy algorithms. The role of inference in a fuzzy environment is also discussed, and a technique for computing fuzzy inferences in FUZZY is examined.

The programming language FUZZY is implemented in LISP, and is currently running on a UNIVAC 1110 computer.     

The three-dimensional fuzzy sets and their cut sets

In this paper, a new kind of L-fuzzy set is introduced which is called the three-dimensional fuzzy set. We first put forward four kinds of cut sets on the three-dimensional fuzzy sets which are defined by the 4-valued fuzzy sets. Then, the definitions of 4-valued order nested sets and 4-valued inverse order nested sets are given. Based on them, the decomposition theorems and representation theorems are obtained. Furthermore, the left interval-valued intuitionistic fuzzy sets and the right interval-valued intuitionistic fuzzy sets are introduced. We show that the lattices constructed by these two special L-fuzzy sets are not equivalent to sublattices of lattice constructed by the interval-valued intuitionistic fuzzy sets. Finally, we show that the three-dimensional fuzzy set is equivalent to the left interval-valued intuitionistic fuzzy set or the right interval-valued intuitionistic fuzzy set.     

Parameterized fuzzy operators in fuzzy decision making

The basic operations of fuzzy sets, such as negation, intersection, and union, usually are computed by applying the one‐complement, minimum, and maximum operators to the membership functions of fuzzy sets. However, different decision agents may have different perceptions for these fuzzy operations. In this article, the concept of parameterized fuzzy operators will be introduced. A parameter α will be used to represent the degree of softness. The variance of α captures the differences of decision agents' subjective attitudes and characteristics, which result in their differing perceptions. The defined parameterized fuzzy operators also should satisfy the axiomatic requirements for the traditional fuzzy operators. A learning algorithm will be proposed to obtain the parameter α given a set of training data for each agent. In this article, the proposed parameterized fuzzy operators will be used in individual decision‐making problems. An example is given to show the concept and application of the parameterized fuzzy operators. © 2003 Wiley Periodicals, Inc.     

Compatibility of t-norms with the concept of ?-partition

In this paper we study the behaviour of a kind of partitions formed by fuzzy sets, the ?-partitions, with respect to three important operations: refinement, union and product of partitions. In the crisp set theory, the previous operations lead to new partitions: every refinement of a partition is also a partition; the union of partitions of disjoint sets is a partition of the union set; the product of two partitions of two sets is a partition of the intersection of the partitioned sets. It has been proven that ?-partitions extend the three previous properties when the intersection of fuzzy sets is defined by the minimum t-norm and the union by the maximum t-conorm. In this paper we consider any t-norm defining the intersection of fuzzy sets and we characterize those t-norms for which refinements, unions and products of ?-partitions are ?-partitions. We pay special attention to these characterizations in the case of continuous t-norms.     

直觉模糊集的结构化分析

直觉模糊集理论采用隶属度函数和非隶属度函数刻画不确定性信息,具有一定程度的主观性。为了研究直觉模糊集的本质特征,提出一种直觉模糊集的结构化分析方法,定义了直觉模糊相容关系,给出了直觉模糊集的同构原理,讨论了直觉模糊集的结构化特征。所得结果表明,直觉模糊集也具有客观性的一面。     

USING META-PROGRAMMING,CONSTRAINT LOGIC PROGRAMMING AND APPROXIMATE REASONING FOR EXPERT SYSTEMS DESIGN

Abstract

This paper presents an enhancement of the CARESS system—A Constraint Approximative Reasoning System Support—introduced in (Popescu and Roventa, 1994). CARESS is an experimental system with primarily two objectives:

(1)knowledge representation and manipulation techniques and to implement them in PROLOG III, and

(2) to develop a knowledge programming environment for building expert systems. We discuss here the use of meta-programming, constraint logic programming and approximate reasoning for the design of expert systems

It has already been proven that meta-programming and logic programming are powerful techniques for expert system design. Fuzzy logic can be used to model one kind of uncertainty. Constraint logic programming is useful for dealing with the constraints given by operations using fuzzy sets.     

RELATIVE UNCERTAINTY AND EVIDENCE SETS: A CONSTRUCTIVIST FRAMEWORK

Abstract

Several measures of uncertainty, in its various forms of nonspecificity, conflict, and fuzziness, valid both in finite and infinite domains are investigated. It is argued that dimensionless measures, relating uncertainty situations to the information content of their respective universal sets, can capture uncertainty efficiently both in finite and infinite domains. These measures are also considered more intuitive. To establish them, a more general approach to uncertainly measures is developed. After this, the utilization of these measures is exemplified in the measurement of the uncertainty content of evidence sets. These interval-based set structures, defined through evidence theory, are shown to possess ideal characteristics for the modeling of human cognitive categorization processes, within a constructivist framework.     

MEMBERSHIP IN A COMPOUND FUZZY SUBSET

Abstract

We first provide a new formulation for a fuzzy subset as a joint relation of the variables, the element and the membership grade of the element. This relationship is developed by appreciating the fact that a fuzzy subset, as is any set, is a union of the elements which constitute it. With a compound fuzzy subset of X defined as a fuzzy subset defined over a base set whose elements are fuzzy subsets of X we suggest a method for obtaining the membership grade of the elements of X in this compound fuzzy subset. This method uses the formulation suggested at the beginning.     

覆盖模糊S-粗集模型

建立了基于覆盖理论的模糊S-粗糙集模型,并讨论其性质。在覆盖单向S-粗集x的最小描述的基础上,给出了x的最大描述的定义。给出了覆盖模糊S-粗集上 、下近似算子定义,讨论了算子的基本性质,证明了覆盖S-粗糙集模型下所有模糊集的下近似构成一个模糊拓扑,并得到模糊单向S-粗集X相对于覆盖单向S-粗集和覆盖约简单向S-粗集的上下近似分别相等。     

Complex neutrosophic set

Complex fuzzy sets and complex intuitionistic fuzzy sets cannot handle imprecise, indeterminate, inconsistent, and incomplete information of periodic nature. To overcome this difficulty, we introduce complex neutrosophic set. A complex neutrosophic set is a neutrosophic set whose complex-valued truth membership function, complex-valued indeterminacy membership function, and complex-valued falsehood membership functions are the combination of real-valued truth amplitude term in association with phase term, real-valued indeterminate amplitude term with phase term, and real-valued false amplitude term with phase term, respectively. Complex neutrosophic set is an extension of the neutrosophic set. Further set theoretic operations such as complement, union, intersection, complex neutrosophic product, Cartesian product, distance measure, and δ-equalities of complex neutrosophic sets are studied here. A possible application of complex neutrosophic set is presented in this paper. Drawbacks and failure of the current methods are shown, and we also give a comparison of complex neutrosophic set to all such methods in this paper. We also showed in this paper the dominancy of complex neutrosophic set to all current methods through the graph.

     

On averaging operators for Atanassov’s intuitionistic fuzzy sets

Atanassov’s intuitionistic fuzzy set (AIFS) is a generalization of a fuzzy set. There are various averaging operators defined for AIFSs. These operators are not consistent with the limiting case of ordinary fuzzy sets, which is undesirable. We show how such averaging operators can be represented by using additive generators of the product triangular norm, which simplifies and extends the existing constructions. We provide two generalizations of the existing methods for other averaging operators. We relate operations on AIFS with operations on interval-valued fuzzy sets. Finally, we propose a new construction method based on the ?ukasiewicz triangular norm, which is consistent with operations on ordinary fuzzy sets, and therefore is a true generalization of such operations.     

Grey集上的逻辑和运算算子

通过定义单位区间I的全体灰信息域上的偏序关系,给出grey集和grey关系在一个格上的定义。由单位区间上的模糊逻辑和运算算子,依据经典扩张原理构造了定义在Θ（I）上相应的grey算子。由格运算的性质导出grey关系合成运算的表示,依照模糊化算子和判决化算子的定义得出对应的grey算子。讨论有关算子的基本性质并举例说明其应用。通过对grey语义的语言值上一组运算的数学描述,旨在提高信息系统对灰信息的处理能力,将处理范围由模糊语言值拓展到基于gray语义的语言值。     

基于模糊集的蚁群空间聚类方法研究

定义了对象间的平均距离,并将平均距离作为对象相似性的论域。通过隶属函数将对象间的相似性映射为论域上的一个模糊子集。由给定的置信水平λ,将模糊集分离为普通集,对蚂蚁是否拾起还是放下对象作出决策,实现对空间数据的聚类。并以矿山实际测量数据为空间数据源,采用基本的蚁群聚类算法和模糊蚁群空间聚类算法分别对其进行聚类。通过对这两种算法的实验结果进行分析比较,证明改进后的算法提高了聚类效果。     

Algorithm and axiomatization of rough fuzzy sets based finite dimensional fuzzy vectors

Rough sets, proposed by Pawlak and rough fuzzy sets proposed by Dubois and Prade were expressed with the different computing formulas that were more complex and not conducive to computer operations. In this paper, we use the composition of a fuzzy matrix and fuzzy vectors in a given non-empty finite universal, constitute an algebraic system composed of finite dimensional fuzzy vectors and discuss some properties of the algebraic system about a basis and operations. We give an effective calculation representation of rough fuzzy sets by the inner and outer products that unify computing of rough sets and rough fuzzy sets with a formula. The basis of the algebraic system play a key role in this paper. We give some essential properties of the lower and upper approximation operators generated by reflexive, symmetric, and transitive fuzzy relations. The reflexive, symmetric, and transitive fuzzy relations are characterized by the basis of the algebraic system. A set of axioms, as the axiomatic approach, has been constructed to characterize the upper approximation of fuzzy sets on the basis of the algebraic system.