2018-02-21T04:31:20Z
http://ijfs.usb.ac.ir/?_action=export&rf=summon&issue=83
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2006
3
1
Cover Vol.3, No.1, April 2006
2006
04
29
0
http://ijfs.usb.ac.ir/article_2916_1a6af1b35d39951bbd11e6046e6554d9.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2006
3
1
A PRIMER ON FUZZY OPTIMIZATION MODELS AND METHODS
J. M.
Cadenas
J. L.
Verdegay
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.
Fuzzy linear programming
Fuzzy optimization
Heuristics algorithms
Intelligent systems
Decision support systems
2006
04
10
1
21
http://ijfs.usb.ac.ir/article_425_a0ec1a73add0c4846c536373c12054c8.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2006
3
1
FIXED POINT THEOREM ON INTUITIONISTIC FUZZY METRIC SPACES
Mohd.
Rafi Segi Rahmat
Mohd.
Salmi Md. Noorani
In this paper, we introduce intuitionistic fuzzy contraction mappingand prove a fixed point theorem in intuitionistic fuzzy metric spaces.
Intuitionistic fuzzy metric spaces
Fuzzy metric spaces
Fixed point
theorem
2006
04
10
23
29
http://ijfs.usb.ac.ir/article_428_2182e75fc67b80369732d9e83a7d92ed.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2006
3
1
FUZZY CONTROL CHARTS FOR VARIABLE AND ATTRIBUTE QUALITY CHARACTERISTICS
MOHAMMAD HASSAN
FAZEL ZARANDI
ISMAIL BURHAN
TURKSEN
ALI
HUSSEINIZADEH KASHAN
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.
Process control
Control chart
Quality characteristics
Fuzzy numbers
2006
04
10
31
44
http://ijfs.usb.ac.ir/article_429_fd5a9eb84c5b612b5f6fb878ed767f8d.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2006
3
1
SOME INTUITIONISTIC FUZZY CONGRUENCES
Kul
Hur
Su Youn
Jang
Hee won
Kang
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}.
T-pure semigroup
Intuitionistic fuzzy set
intuitionistic fuzzy congruence
intuitionistic fuzzy group congruence
intuitionistic fuzzy semilattice congruence
intuitionistic
fuzzy normal congruence
2006
04
10
45
57
http://ijfs.usb.ac.ir/article_436_f22fe30fd03b80f8f93124348aec9f90.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2006
3
1
GENERALIZED FUZZY POLYGROUPS
B.
Davvaz
P.
Corsini
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.
Polygroups fuzzy set
($epsilon$
$epsilon$ $vee$ q)-fuzzy subpolygroup
Fuzzy Logic
Implication operator
2006
04
10
59
75
http://ijfs.usb.ac.ir/article_438_60af777c569da9c45db7ad29f576cf8a.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2006
3
1
NEW CRITERIA FOR RULE SELECTION IN FUZZY LEARNING CLASSIFIER SYSTEMS
MEHDI
EFTEKHARI
MANSOUR
ZOLGHADRI JAHROMI
SERAJEDDIN
KATEBI
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.
Fuzzy classification
Rule evaluation criteria
Information retrieval
Iterative rule learning
2006
04
10
77
89
http://ijfs.usb.ac.ir/article_439_f7e8f096de34f37fde594f63919275d0.pdf
Iranian Journal of Fuzzy Systems
IJFS
1735-0654
1735-0654
2006
3
1
Persian-translation Vol.3, No.1, April 2006
2006
04
29
93
98
http://ijfs.usb.ac.ir/article_2917_034ae857d5d5d22294410cd6729fff7e.pdf