Cover Vol.3, No.1, April 2006
text
article
2006
eng
Iranian Journal of Fuzzy Systems
University of Sistan and Baluchestan
1735-0654
3
v.
1
no.
2006
0
http://ijfs.usb.ac.ir/article_2916_1a6af1b35d39951bbd11e6046e6554d9.pdf
dx.doi.org/10.22111/ijfs.2006.2916
A PRIMER ON FUZZY OPTIMIZATION MODELS AND METHODS
J. M.
Cadenas
Departamento de Ingenier´ıa de la Informaci´on y las Comunicaciones.
Facultad de Inform´atica., Universidad de Murcia., Campus de Espinardo. 30071-Espinardo.
Murcia, Spain
author
J. L.
Verdegay
Departamento de Ciencias de la Computaci´on e Inteligencia Artificial.
E.T.S. de Ingenier´ıa Inform´atica, Universidad de Granada., 18071. Granada,
Spain
author
text
article
2006
eng
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.
Iranian Journal of Fuzzy Systems
University of Sistan and Baluchestan
1735-0654
3
v.
1
no.
2006
1
21
http://ijfs.usb.ac.ir/article_425_a0ec1a73add0c4846c536373c12054c8.pdf
dx.doi.org/10.22111/ijfs.2006.425
FIXED POINT THEOREM ON INTUITIONISTIC FUZZY METRIC SPACES
Mohd.
Rafi Segi Rahmat
School of Mathematical Science, Faculty of Science and
Technology, Universiti kebangsaan Malaysia, 43600 Bangi, Selangor D.E., Malaysia
author
Mohd.
Salmi Md. Noorani
School of Mathematical Science, Faculty of Science and
Technology, Universiti kebangsaan Malaysia, 43600 Bangi, Selangor D.E., Malaysia
author
text
article
2006
eng
In this paper, we introduce intuitionistic fuzzy contraction mappingand prove a fixed point theorem in intuitionistic fuzzy metric spaces.
Iranian Journal of Fuzzy Systems
University of Sistan and Baluchestan
1735-0654
3
v.
1
no.
2006
23
29
http://ijfs.usb.ac.ir/article_428_2182e75fc67b80369732d9e83a7d92ed.pdf
dx.doi.org/10.22111/ijfs.2006.428
FUZZY CONTROL CHARTS FOR VARIABLE AND ATTRIBUTE QUALITY CHARACTERISTICS
MOHAMMAD HASSAN
FAZEL ZARANDI
DEPARTMENT OF INDUSTRIAL ENGINEERING, AMIRKABIR
UNIVERSITY OF TECHNOLOGY, TEHRAN, IRAN
author
ISMAIL BURHAN
TURKSEN
DEPARTMENT OF MECHANICAL AND INDUSTRIAL ENGINEERING, UNIVERSITY
OF TORONTO, TORONTO, ON, CANADA, M5S2H8
author
ALI
HUSSEINIZADEH KASHAN
DEPARTMENT OF INDUSTRIAL ENGINEERING, AMIRKABIR UNIVERSITY OF
TECHNOLOGY, P. O. BOX: 15875-4413, TEHRAN, IRAN
author
text
article
2006
eng
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.
Iranian Journal of Fuzzy Systems
University of Sistan and Baluchestan
1735-0654
3
v.
1
no.
2006
31
44
http://ijfs.usb.ac.ir/article_429_fd5a9eb84c5b612b5f6fb878ed767f8d.pdf
dx.doi.org/10.22111/ijfs.2006.429
SOME INTUITIONISTIC FUZZY CONGRUENCES
Kul
Hur
Division of Mathematics and Informational Statistics, and
Institute of Basic Natural Science, Wonkwang University, Iksan, Chonbuk, Korea 570-
749
author
Su Youn
Jang
Division of Mathematics and Informational Statistics, and
Institute of Basic Natural Science, Wonkwang University, Iksan, Chonbuk, Korea 570-
749
author
Hee won
Kang
Dept. of Mathematics Education, Woosuk University, Hujong-Ri
Samrae-Eup, Wanju-kun Chonbuk, Korea 565-701
author
text
article
2006
eng
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}.
Iranian Journal of Fuzzy Systems
University of Sistan and Baluchestan
1735-0654
3
v.
1
no.
2006
45
57
http://ijfs.usb.ac.ir/article_436_f22fe30fd03b80f8f93124348aec9f90.pdf
dx.doi.org/10.22111/ijfs.2006.436
GENERALIZED FUZZY POLYGROUPS
B.
Davvaz
Department of Mathematics, Yazd University, Yazd, Iran
author
P.
Corsini
Dipartimento Di Matematica E Informatica, Via Delle Scienze 206, 33100
Udin, Italy
author
text
article
2006
eng
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.
Iranian Journal of Fuzzy Systems
University of Sistan and Baluchestan
1735-0654
3
v.
1
no.
2006
59
75
http://ijfs.usb.ac.ir/article_438_60af777c569da9c45db7ad29f576cf8a.pdf
dx.doi.org/10.22111/ijfs.2006.438
NEW CRITERIA FOR RULE SELECTION IN FUZZY LEARNING CLASSIFIER SYSTEMS
MEHDI
EFTEKHARI
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING, SHIRAZ UNIVERSITY,
SHIRAZ, IRAN
author
MANSOUR
ZOLGHADRI JAHROMI
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING, SHIRAZ
UNIVERSITY, SHIRAZ, IRAN
author
SERAJEDDIN
KATEBI
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING, SHIRAZ UNIVERSITY,
SHIRAZ, IRAN
author
text
article
2006
eng
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.
Iranian Journal of Fuzzy Systems
University of Sistan and Baluchestan
1735-0654
3
v.
1
no.
2006
77
89
http://ijfs.usb.ac.ir/article_439_f7e8f096de34f37fde594f63919275d0.pdf
dx.doi.org/10.22111/ijfs.2006.439
Persian-translation Vol.3, No.1, April 2006
text
article
2006
eng
Iranian Journal of Fuzzy Systems
University of Sistan and Baluchestan
1735-0654
3
v.
1
no.
2006
93
98
http://ijfs.usb.ac.ir/article_2917_034ae857d5d5d22294410cd6729fff7e.pdf
dx.doi.org/10.22111/ijfs.2006.2917