Showing papers in "Fuzzy Sets and Systems in 1998"

TL;DR: A new method for ranking fuzzy numbers by distance method, based on calculating the centroid point, which can rank more than two fuzzy numbers simultaneously, and the fuzzy numbers need not be normal.

TL;DR: This paper uses two quantifier guided choice degrees of alternatives, a dominance degree used to quantify the dominance that one alternative has over all the others, in a fuzzy majority sense, and a non dominance degree, that generalises Orlovski's non dominated alternative concept.

TL;DR: A fuzzy simulation based genetic algorithm is designed for solving chance constrained programming from stochastic to fuzzy environments and some numerical examples are discussed.

TL;DR: This work investigates the issue of obtaining the weights associated with the OWA aggregation in the situation when the authors have observed data on the arguments and the aggregated value and introduces a family of OWA operators called exponential OWA Operators.

TL;DR: A general fuzzy linear system is investigated using the embedding approach and conditions for the existence of a unique fuzzy solution to n × n linear system are derived and a numerical procedure for calculating the solution is designed.

TL;DR: A new definition for the lower approximation operator A M : IXPIX of a fuzzy „-rough set" satisfies the two important identities AM A M "A M and A M AM "AM, as well as a number of other interesting properties, and provides axiomatics to fully characterize those upper and lower approximations.

TL;DR: A new method to forecast university enrollments is proposed, where the historical enrollments of the University of Alabama shown in [7,8] are used to illustrate the forecasting process.

TL;DR: This paper proposes two parameters, value and ambiguity, for fuzzy number representation and uses them to obtain canonical representations and to deal with fuzzy numbers in decision-making problems.

TL;DR: A genetic learning process for learning fuzzy control rules from examples is developed in three stages: the first one is a fuzzy rule genetic generating process based on a rule learning iterative approach, the second one combines two kinds of rules, experts rules if there are and the previously generated fuzzy controlrules.

TL;DR: A modification of fuzzy linear regression analysis based on a criterion of minimizing the difference of the fuzzy membership values between the observed and estimated fuzzy numbers is proposed.

TL;DR: This paper proves that the hierarchical fuzzy systems are universal approximators; that is, they can approximate any nonlinear function on a compact set to arbitrary accuracy.

TL;DR: A fuzzy simulation based genetic algorithm is employed to solve a numerical example of nonlinear chance constrained programming as well as multiobjective case and goal programming with fuzzy coefficients occurring in not only constraints but also objectives.

TL;DR: A method of ranking fuzzy numbers based on these metrics is proposed and investigated, and not only fuzzy numbers but also the left-sided and right-sided fuzzy numbers are considered.

TL;DR: The fuzzy model proposed by Takagi and Sugeno can represent highly nonlinear systems and is widely used for the representation of fuzzy rules and an identification scheme for rule's premise and consequence parameters is deduced from the clustering algorithm in succession.

TL;DR: The algorithm to construct automatically the FCM is described, the case study is provided to illustrate the functioning of the algorithm, and the use of the fuzzy expert system tool (FEST) is provided.

TL;DR: In this paper the design of fuzzy sliding-mode control is discussed and conditions for the fuzzy sliding mode control to stabilize the global fuzzy model are given.

TL;DR: A learning algorithm based on soft consistency and completeness conditions is proposed that combines in a single process rule and feature selection and it is tested on different databases.

TL;DR: In this paper, interval-valued fuzzy numbers and interval-distribution numbers are defined and their extended operations are given, and some applications of generalized pseudo-probability metric spaces and pseudo-metric spaces are discussed.

TL;DR: In the present paper, the authors define F-open sets,F-closed sets, F-adherent points, F -limit points, and F-isolated points to make some characterizations of fuzzy metric spaces.

TL;DR: Different to classical logic, various semantics of propositions in fuzzy logic fall into different frameworks which are known as the possibility distribution-based reasoning system and the many-valued fuzzy logics.

TL;DR: The general restrictions on design-appropriate aggregation functions are outlined, and a family of functions, modeling a range of trade-off strategies, is presented.

TL;DR: A pseudo-metric on the set of fuzzy numbers arising from the idea of the value of a fuzzy number is described, and some of its topological properties are noted.

TL;DR: Several choice processes of alternatives for non-homogeneous group decision-making problems, assuming linguistic preference relations for expressing the opinions of individuals and linguistic values for expressing their respective power or importance degrees, are presented.

TL;DR: This paper deals with two of the ways in which uncertainty can appear in theSolid transportation problem: Interval solid transportation problem and fuzzy solid transportationProblem, where data problem are expressed as intervals instead of point values.

TL;DR: This paper presents a multistage random sampling fuzzy c-means-based clustering algorithm, which significantly reduces the computation time required to partition a data set into c classes.

TL;DR: The results extend, generalize and fuzzify several fixed point theorems on metric spaces, Menger probabilistic metric Spaces, uniform spaces and fuzzy metric spaces.

TL;DR: This paper constructs two versions of intuitionistic fuzzy propositional calculus (IFPC) and a version of intuitionists fuzzy predicate logic (IFPL) that are based on the notion of intuitionistically fuzzy set.

TL;DR: An algorithm has been proposed which solves the transportation problem with fuzzy supply and demand values and the integrality condition imposed on the solution and is exact and computationally effective.

TL;DR: A new interpolative reasoning method to deal with fuzzy reasoning in sparse rule-based systems is presented which can overcome the drawback of Koczy and Hirota's method described in [7].

TL;DR: F fuzzy implications of fuzzy cognitive map (FCM), which has been used in various fields for representing causal knowledge, are discussed and new fuzzy implications are revealed and used for forming causal knowledge.