Group decision making using fuzzy sets theory for evaluating the rate of aggregative risk in software development

The purpose of this study is not only to build a group decision making structure model of risk in software development but also to propose two algorithms to tackle the rate of aggregative risk in a fuzzy environment by fuzzy sets theory during any phase of the life cycle. While evaluating the rate of aggregative risk, one may adjust or improve the weights or grades of the factors until she/he can accept it. Moreover, our result will be more objective and unbiased since it is generated by a group of evaluators.     

Two fuzzier implication operators in the theory of fuzzy power sets

The theory of fuzzy power sets requires the use of an implication operator acting within the set of values taken by the membership functions of the fuzzy sets. Two such operators and resulting relationships between fuzzy sets are studied here, and the results compared with previous ones obtained with other implication operators.     

Development of an energy-emission model using fuzzy sets

In searching for cost-efficient strategies to reduce emissions from energy conversion, most western countries use energy-emission models. In these models, the whole energy conversion chain and possible future options for energy supply and emission reduction are mapped into a network of energy flows. Total discounted cost of energy supply and emission reduction is minimized under the restriction of maximum allowed emissions of SO₂, NO _x , or CO₂. The present paper extends one of these models to allow for fuzzy parameters. Such an extension appears to be useful when the data situation is weak. In this paper, a fuzzy linear program is developed, which has been applied to an energy-emission model of Lithuania.     

A class of linear differential dynamical systems with fuzzy matrices

This paper investigates the first order linear fuzzy differential dynamical systems with fuzzy matrices. We use a complex number representation of the α-level sets of the fuzzy system, and obtain the solution by employing such representation. It is applicable to practical computations and has also some implications for the theory of fuzzy differential equations. We then present some properties of the 2-dimensional dynamical systems and their phase portraits. Some examples are considered to show the richness of the theory and we can clearly see that new behaviors appear. We finally present some conclusions and new directions for further research in the area of fuzzy dynamical systems.     

Nonprobabilistic entropies and indetermination measures in the setting of fuzzy sets theory

Our purpose in this paper is first of all to build an axiomatic generalization for the nonprobabilistic entropy of De Luca and Termini in the setting of fuzzy sets theory.We then build from this entropy an indetermination measure which can be used like discriminant function in Pattern Recognition when patterns are described by means of fuzzy sets.     

Fuzzy group theory: A comparison of different notions of product of fuzzy sets

The notion of penultimate subgroup of a fuzzy group is introduced. This leads to the concept of penultimate product of fuzzy subgroups. A detailed study of different notions of product of fuzzy sets in a groupoid is carried out. The ultimate superiority of Liu's set product is established. Level subsets and strong level subsets of these products are also analysed. Consequently, simplified proofs of some of the results on free product are obtained.     

Mixed aleatory and epistemic uncertainty quantification using fuzzy set theory

This paper proposes algorithms to construct fuzzy probabilities to represent or model the mixed aleatory and epistemic uncertainty in a limited-size ensemble. Specifically, we discuss the possible requirements for the fuzzy probabilities in order to model the mixed types of uncertainty, and propose algorithms to construct fuzzy probabilities for both independent and dependent datasets. The effectiveness of the proposed algorithms is demonstrated using one-dimensional and high-dimensional examples. After that, we apply the proposed uncertainty representation technique to isocontour extraction, and demonstrate its applicability using examples with both structured and unstructured meshes.     

Semantics and pragmatics of fuzzy sets and systems

A philosophical formalism of a new methodological aspect of humanistic systems design and evaluation is given. A requisite concept of context-dependency is highlighted, and some approaches to fuzzy sets and linguistics subsequently extended. It is consequently shown that a mathematical theory of pragmatic fuzzy subsets is not only conceptually possible but practically implementable in man-machine studies as well. Thus, the important context-dependent implication of the subjective nature of Zadeh's theory of fuzzy sets can be better exploited.As a pragmatic theory, the approach here is a seeming connection between pragmatism and ontology, concepts that are traditionally diametrically opposed to each other. The attitude adopted has been the equation of pragmatism and psychophysical measurements of ontological objects (noumena). Pragmatism is tacitly defined as a form of empiricism whereby linguistic constructs (i.e., linguistic-variable denotions) that represent any aspect(s) of a humanistic system are nothing more than an operational procedure used to achieve psychophysical measurements of the aspect(s). In this fashion, pragmatism would enable the contents of assertions, which are made through declarative propositions, about humanistic systems to be deciphered within relevant contexts. For pragmatism, direct sense experience provides both the meaning and the criterion of reality judgements. The context-dependent nature of the physical reality of ontological entities is, therefore, better understood pragmatically vis-a-vis the appropriate evaluative criteria and interpretative conventions.By emphasizing the distinction between a fuzzy system (naturally fuzzified) and a fuzzified mathematical structure (meta-mathematically fuzzified), the use of the standard fuzzy topologies is justified although it is conceptually possible to develop a yet more general topology or perhaps an alternative one especially in the case of a meta-mathematically fuzzified structure. However, for the express purpose of machine implementation, a naturally fuzzified system, such as a (complex) humanistic system, is more amenable to an initial test of the philosophy of pragmatic fuzzy subsets. Consequently, the fuzzy topology and spaces employed are intended to be devoid of extensive generalities, in this instance.     

Comparison of fuzzy numbers using a fuzzy distance measure

A new approach for ranking fuzzy numbers based on a distance measure is introduced. A new class of distance measures for interval numbers that takes into account all the points in both intervals is developed first, and then it is used to formulate the distance measure for fuzzy numbers. The approach is illustrated by numerical examples, showing that it overcomes several shortcomings such as the indiscriminative and counterintuitive behavior of several existing fuzzy ranking approaches.     

Attribute reduction based on generalized fuzzy evidence theory in fuzzy decision systems

Attribute reduction is viewed as an important issue in data mining and knowledge representation. This paper studies attribute reduction in fuzzy decision systems based on generalized fuzzy evidence theory. The definitions of several kinds of attribute reducts are introduced. The relationships among these reducts are then investigated. In a fuzzy decision system, it is proved that the concepts of fuzzy positive region reduct, lower approximation reduct and generalized fuzzy belief reduct are all equivalent, the concepts of fuzzy upper approximation reduct and generalized fuzzy plausibility reduct are equivalent, and a generalized fuzzy plausibility consistent set must be a generalized fuzzy belief consistent set. In a consistent fuzzy decision system, an attribute set is a generalized fuzzy belief reduct if and only if it is a generalized fuzzy plausibility reduct. But in an inconsistent fuzzy decision system, a generalized fuzzy belief reduct is not a generalized fuzzy plausibility reduct in general.     

Applications of fuzzy sets in curve fitting

This paper presents several methods for functional approximation with variable-knot variable-degree splines, with variable-knot first order splines, which are relatively easy to find, as the intermediate input. By means of a fuzzy characteristic function determining how “sharp” the angle at each knot is (the higher the polynomial degree, the “sharper” its first order spline approximation bends), we can decide whether we can group certain adjacent segments together to be approximated by a single higher order polynomial segment. Some simulation experiments have also been done.     

A comparison of two methods for determining the weights of belonging to fuzzy sets

Saaty has solved a basic problem in fuzzy set theory using an eigenvector method to determine the weights of belonging of each member to the set. In this paper, a weighted least-square method is utilized to obtain the weights. This method has the advantage that it involves the solution of a set of simultaneous linear algebraic equations and is thus conceptually easier to understand than the eigenvector method. Examples are given for estimating the relative wealth of nations and the relative amount of foreign trade of nations. Numerical solutions are obtained using both the eigenvector method and the weighted least-square method, and the results are compared.Partial support of this research was provided by NIH Grant No. GM-23732.     

On a general class of fuzzy connectives

A general class of conectives, intersection and union, are presented for fuzzy sets. The properties of this class are studied in comparison to the ordinary intersection and union.     

Modeling fuzzy information in UML class diagrams and object-oriented database models

Conceptual data modeling has become essential for non-traditional application areas. Some conceptual data models have been proposed as tools for database design and object-oriented database modeling. Information in real-world applications is often vague or ambiguous. Currently, a little research is underway on modeling the imprecision and uncertainty in conceptual data modeling and the conceptual design of fuzzy databases. The unified modeling language (UML) is a set of object-oriented modeling notations and a standard of the object management group (OMG) with applications to many areas of software engineering and knowledge engineering, increasingly including data modeling. This paper introduces different levels of fuzziness into the class of UML and presents the corresponding graphical representations, with the result that UML class diagrams may model fuzzy information. The fuzzy UML data model is also formally mapped into the fuzzy object-oriented database model.     

The role of fuzzy sets in civil engineering

Civil Engineering is an art, the practise of which requires the use of scientific knowledge as a basic tool. The engineer has to use considerable judgement in setting up and interpreting his scientific calculations and in making decisions based upon incomplete information. Because a very high safety level is required in civil engineering structures the uncertainty associated with the application of scientific calculations is very crudely and conservatively estimated using traditional methods. Current reliability theory enables a discussion of random parameter uncertainty but in civil engineering, system uncertainty and the possibility of human error is extremely important and must be included in any reliability calculations.The potential role of fuzzy sets in analysing system and human uncertainty is discussed in the paper and two numerical examples are given of the calculation of system and random uncertainty using probability measures of fuzzy sets.     

On the algebraic solution of fuzzy linear systems based on interval theory

In this paper, fuzzy linear systems involving a crisp square matrix and a fuzzy right-hand side vector are considered. A new approach to solve such systems based on interval theory and the new concept “interval inclusion linear system” is proposed. Also, new necessary and sufficient conditions are derived for obtaining the unique algebraic solution. Numerical examples are given to illustrate the efficiency of the proposed method.     

On a family of connectives for fuzzy sets

In this paper we find the general solution of the functional equation $\text{S}{}^1\text{(T(x, y), T(x, N(y))) = x}$, where $\text{S}{}^1$ is a t-conorm, T is a t-norm and N is a strong negation on the unit interval. In particular the result yields a family of connectives for fuzzy sets.     

The reduction and fusion of fuzzy covering systems based on the evidence theory

This paper studies reduction of a fuzzy covering and fusion of multi-fuzzy covering systems based on the evidence theory and rough set theory. A novel pair of belief and plausibility functions is defined by employing a method of non-classical probability model and the approximation operators of a fuzzy covering. Then we study the reduction of a fuzzy covering based on the functions we presented. In the case of multiple information sources, we present a method of information fusion for multi-fuzzy covering systems, by which objects can be well classified in a fuzzy covering decision system. Finally, by using the method of maximum flow, we discuss under what conditions, fuzzy covering approximation operators can be induced by a fuzzy belief structure.