Top Research Papers on Fuzzy Set Theory

A description of fuzzy indexing procedures defined to represent the varying significance of terms in synthesizing the documents’ contents and some applications to model flexible information retrieval systems are presented.

In this review, the basic mathematical framework of fuzzy set theory will be described, as well as the most important applications of this theory to other theories and techniques.

The following concepts are covered for standard fuzzy sets: ?

Fuzzy set theory was introduced in 1965 by Dr. Lotfi Zadeh to represent/manipulate data and information possessing nonstatistical uncertainties and allows for much more realistic models of real-world situations.

A quick overview of developing fuzzy sets methodologies in actuarial science, and the basic mathematics of fuzzy sets is provided.

A quick overview of developing fuzzy sets methodologies in actuarial science, and the basic mathematics of fuzzy sets is provided.

Fuzzy set theory and fuzzy logic provide a different way to view the problem of modeling uncertainty and offer a wide range of computational tools to aid decision making.

An emerging group of successful parties in Central and Eastern "Concept Structures and Fuzzy Set Theory: A Proposal for Concept" and an in-depth understanding of rough set theory is discussed.

The authors define equality of two soft sets, subset and super set of a soft set, complement of a Soft complement function, NOT set, null soft set and absolute soft set with examples.

Fuzzy linguistic variables and fuzzy algorithms offer an effective, more flexible way to describe a system's behavior too complex for a classical mathematical model and are very successful in economics, management science, artificial intelligence, information retrieval systems, pattern recognition, image processing, psychology, biology, and other fields rendered inherently fuzzy.

An introduction to fuzzy set theory is described. Topics covered include: neural networks and fuzzy systems; the dynamical systems approach to machine intelligence; intelligent behavior as adaptive model-free estimation; fuzziness versus probability; fuzzy sets; the entropy-subsethood theorem; adaptive fuzzy systems for backing up a truck-and-trailer; product-space clustering with differential competitive learning; and adaptive fuzzy system for target tracking.

It is found that the existing definition of complementation as well as the probability - possibility consistency principles is not well defined and the results obtained would be inappropriate fro m the standpoint of the Randomness-Fuzziness consistency principles.

The fuzzification of k-ideals (also r-ideal) of inclines is considered, and then it is proved the notion of fuzzy ideal and fuzzy k-Ideal coincide, and some properties on fuzzy r-Ideals are investigated.

This paper proposes a possibility of developing an axiomatic set theory, as first-order theory within the framework of fuzzy logic in the style of Hajek's Basic fuzzy logic BL, and shows the nontriviality of the theory.

F fuzzy set theory which provides a methodology of treating human subjectivity, linguistic meanings, etc. is reviewed and its novel applications to some actual problems are introduced to show the efficiency of the theory.

The paper shows, in particular, that these theories do always verify the Kleene's law, as well as the Non-Contradiction and Excluded-Middle principles when these are understood as in ancient logic.

Fuzzy set theory has a number of properties that make it suitable for formalizing the uncertain information upon which medical diagnosis and treatment is usually based, and it provides a linguistic approach with an excellent approximation to texts.

It is demonstrated that fuzzy set theory is applicable to a wide range of practical problems and that simple fuzzy control algorithms do give good results.

The purpose of this paper is to introduce fuzzy set theory which has a number of properties that makes it suitable for formalizing the uncertain information in dealing with systems comprising a very large number of interacting elements or involving aLarge number of variables in their decision trees.

This section of HSM will also put forward some concerns regarding present attitudes towards the theory of fuzzy sets and its applicability III management-oriented fields of research.