unavailable

unavailable

Fuzzy Linear Programming models and methods has been one ofthe most and well studied topics inside the broad area of Soft Computing. Itsapplications as well as practical realizations can be found in all the real worldareas. In this paper a basic introduction to the main models and methods infuzzy mathematical programming, with special emphasis on those developedby the authors, is presented. As a whole, Linear Programming problems withfuzzy costs, fuzzy constraints and fuzzy coefficients in the technological matrixare analyzed. Finally, future research and development lines are also pointedout by focusing on fuzzy sets based heuristic algorithms.

In this paper, we introduce intuitionistic fuzzy contraction mappingand prove a fixed point theorem in intuitionistic fuzzy metric spaces.

This paper addresses the design of control charts for both variable ( x chart) andattribute (u and c charts) quality characteristics, when there is uncertainty about the processparameters or sample data. Derived control charts are more flexible than the strict crisp case, dueto the ability of encompassing the effects of vagueness in form of the degree of expert’spresumption. We extend the use of proposed fuzzy control charts in case of linguistic data using adeveloped defuzzifier index, which is based on the metric distance between fuzzy sets.

First, we introduce the concept of intuitionistic fuzzy group congruenceand we obtain the characterizations of intuitionistic fuzzy group congruenceson an inverse semigroup and a T^{*}-pure semigroup, respectively. Also,we study some properties of intuitionistic fuzzy group congruence. Next, weintroduce the notion of intuitionistic fuzzy semilattice congruence and we givethe characterization of intuitionistic fuzzy semilattice congruence on a T^{*}-puresemigroup. Finally, we introduce the concept of intuitionistic fuzzy normalcongruence and we prove that (IFNC(E_{S}), $cap$, $vee$) is a complete lattice. Andwe find the greatest intuitionistic fuzzy normal congruence containing an intuitionisticfuzzy congruence on E_{S}.

small Polygroups are multi-valued systems that satisfy group-likeaxioms. Using the notion of “belonging ($epsilon$)” and “quasi-coincidence (q)” offuzzy points with fuzzy sets, the concept of ($epsilon$, $epsilon$ $vee$ q)-fuzzy subpolygroups isintroduced. The study of ($epsilon$, $epsilon$ $vee$ q)-fuzzy normal subpolygroups of a polygroupare dealt with. Characterization and some of the fundamental properties ofsuch fuzzy subpolygroups are obtained. ($epsilon$, $epsilon$ $vee$ q)-fuzzy cosets determined by($epsilon$, $epsilon$ $vee$ q)-fuzzy subpolygroups are discussed. Finally, a fuzzy subpolygroupwith thresholds, which is a generalization of an ordinary fuzzy subpolygroupand an ($epsilon$, $epsilon$ $vee$ q)-fuzzy subpolygroup, is defined and relations between twofuzzy subpolygroups are discussed.

Designing an effective criterion for selecting the best rule is a major problem in theprocess of implementing Fuzzy Learning Classifier (FLC) systems. Conventionally confidenceand support or combined measures of these are used as criteria for fuzzy rule evaluation. In thispaper new entities namely precision and recall from the field of Information Retrieval (IR)systems is adapted as alternative criteria for fuzzy rule evaluation. Several differentcombinations of precision and recall are redesigned to produce a metric measure. These newlyintroduced criteria are utilized as a rule selection mechanism in the method of Iterative RuleLearning (IRL) of FLC. In several experiments, three standard datasets are used to compare andcontrast the novel IR based criteria with other previously developed measures. Experimentalresults illustrate the effectiveness of the proposed techniques in terms of classificationperformance and computational efficiency.

unavailable