University of Sistan and Baluchestan Iranian Journal of Fuzzy Systems 1735-0654 3 1 2006 04 10 A PRIMER ON FUZZY OPTIMIZATION MODELS AND METHODS A PRIMER ON FUZZY OPTIMIZATION MODELS AND METHODS 1 21 425 10.22111/ijfs.2006.425 EN 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 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 Journal Article 2005 10 10 Fuzzy Linear Programming models and methods has been one of the most and well studied topics inside the broad area of Soft Computing. Its applications as well as practical realizations can be found in all the real world areas. In this paper a basic introduction to the main models and methods in fuzzy mathematical programming, with special emphasis on those developed by the authors, is presented. As a whole, Linear Programming problems with fuzzy costs, fuzzy constraints and fuzzy coefficients in the technological matrix are analyzed. Finally, future research and development lines are also pointed out by focusing on fuzzy sets based heuristic algorithms.

https://ijfs.usb.ac.ir/article_425_a0ec1a73add0c4846c536373c12054c8.pdf

University of Sistan and Baluchestan Iranian Journal of Fuzzy Systems 1735-0654 3 1 2006 04 10 FIXED POINT THEOREM ON INTUITIONISTIC FUZZY METRIC SPACES FIXED POINT THEOREM ON INTUITIONISTIC FUZZY METRIC SPACES 23 29 428 10.22111/ijfs.2006.428 EN Mohd. Rafi Segi Rahmat School of Mathematical Science, Faculty of Science and Technology, Universiti kebangsaan Malaysia, 43600 Bangi, Selangor D.E., Malaysia Mohd. Salmi Md. Noorani School of Mathematical Science, Faculty of Science and Technology, Universiti kebangsaan Malaysia, 43600 Bangi, Selangor D.E., Malaysia Journal Article 2005 07 10 In this paper, we introduce intuitionistic fuzzy contraction mapping and prove a fixed point theorem in intuitionistic fuzzy metric spaces.

https://ijfs.usb.ac.ir/article_428_2182e75fc67b80369732d9e83a7d92ed.pdf

University of Sistan and Baluchestan Iranian Journal of Fuzzy Systems 1735-0654 3 1 2006 04 10 FUZZY CONTROL CHARTS FOR VARIABLE AND ATTRIBUTE QUALITY CHARACTERISTICS FUZZY CONTROL CHARTS FOR VARIABLE AND ATTRIBUTE QUALITY CHARACTERISTICS 31 44 429 10.22111/ijfs.2006.429 EN MOHAMMAD HASSAN FAZEL ZARANDI DEPARTMENT OF INDUSTRIAL ENGINEERING, AMIRKABIR UNIVERSITY OF TECHNOLOGY, TEHRAN, IRAN ISMAIL BURHAN TURKSEN DEPARTMENT OF MECHANICAL AND INDUSTRIAL ENGINEERING, UNIVERSITY OF TORONTO, TORONTO, ON, CANADA, M5S2H8 ALI HUSSEINIZADEH KASHAN DEPARTMENT OF INDUSTRIAL ENGINEERING, AMIRKABIR UNIVERSITY OF TECHNOLOGY, P. O. BOX: 15875-4413, TEHRAN, IRAN Journal Article 2005 02 10 This paper addresses the design of control charts for both variable ( x chart) and attribute (u and c charts) quality characteristics, when there is uncertainty about the process parameters or sample data. Derived control charts are more flexible than the strict crisp case, due to the ability of encompassing the effects of vagueness in form of the degree of expert’s presumption. We extend the use of proposed fuzzy control charts in case of linguistic data using a developed defuzzifier index, which is based on the metric distance between fuzzy sets.

https://ijfs.usb.ac.ir/article_429_fd5a9eb84c5b612b5f6fb878ed767f8d.pdf

University of Sistan and Baluchestan Iranian Journal of Fuzzy Systems 1735-0654 3 1 2006 04 10 SOME INTUITIONISTIC FUZZY CONGRUENCES SOME INTUITIONISTIC FUZZY CONGRUENCES 45 57 436 10.22111/ijfs.2006.436 EN Kul Hur Division of Mathematics and Informational Statistics, and Institute of Basic Natural Science, Wonkwang University, Iksan, Chonbuk, Korea 570- 749 Su Youn Jang Division of Mathematics and Informational Statistics, and Institute of Basic Natural Science, Wonkwang University, Iksan, Chonbuk, Korea 570- 749 Hee Won Kang Dept. of Mathematics Education, Woosuk University, Hujong-Ri Samrae-Eup, Wanju-kun Chonbuk, Korea 565-701 Journal Article 2005 03 10 First, we introduce the concept of intuitionistic fuzzy group congruence and we obtain the characterizations of intuitionistic fuzzy group congruences on an inverse semigroup and a T^{*}-pure semigroup, respectively. Also, we study some properties of intuitionistic fuzzy group congruence. Next, we introduce the notion of intuitionistic fuzzy semilattice congruence and we give the characterization of intuitionistic fuzzy semilattice congruence on a T^{*}-pure semigroup. Finally, we introduce the concept of intuitionistic fuzzy normal congruence and we prove that (IFNC(E_{S}), $\cap$, $\vee$) is a complete lattice. And we find the greatest intuitionistic fuzzy normal congruence containing an intuitionistic fuzzy congruence on E_{S}.

https://ijfs.usb.ac.ir/article_436_f22fe30fd03b80f8f93124348aec9f90.pdf

University of Sistan and Baluchestan Iranian Journal of Fuzzy Systems 1735-0654 3 1 2006 04 10 GENERALIZED FUZZY POLYGROUPS GENERALIZED FUZZY POLYGROUPS 59 75 438 10.22111/ijfs.2006.438 EN B. Davvaz Department of Mathematics, Yazd University, Yazd, Iran P. Corsini Dipartimento Di Matematica E Informatica, Via Delle Scienze 206, 33100 Udin, Italy Journal Article 2005 05 10 small Polygroups are multi-valued systems that satisfy group-like axioms. Using the notion of “belonging ($\epsilon$)” and “quasi-coincidence (q)” of fuzzy points with fuzzy sets, the concept of ($\epsilon$, $\epsilon$ $\vee$ q)-fuzzy subpolygroups is introduced. The study of ($\epsilon$, $\epsilon$ $\vee$ q)-fuzzy normal subpolygroups of a polygroup are dealt with. Characterization and some of the fundamental properties of such fuzzy subpolygroups are obtained. ($\epsilon$, $\epsilon$ $\vee$ q)-fuzzy cosets determined by ($\epsilon$, $\epsilon$ $\vee$ q)-fuzzy subpolygroups are discussed. Finally, a fuzzy subpolygroup with thresholds, which is a generalization of an ordinary fuzzy subpolygroup and an ($\epsilon$, $\epsilon$ $\vee$ q)-fuzzy subpolygroup, is defined and relations between two fuzzy subpolygroups are discussed.

https://ijfs.usb.ac.ir/article_438_60af777c569da9c45db7ad29f576cf8a.pdf

University of Sistan and Baluchestan Iranian Journal of Fuzzy Systems 1735-0654 3 1 2006 04 10 NEW CRITERIA FOR RULE SELECTION IN FUZZY LEARNING CLASSIFIER SYSTEMS NEW CRITERIA FOR RULE SELECTION IN FUZZY LEARNING CLASSIFIER SYSTEMS 77 89 439 10.22111/ijfs.2006.439 EN MEHDI EFTEKHARI DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING, SHIRAZ UNIVERSITY, SHIRAZ, IRAN MANSOUR ZOLGHADRI JAHROMI DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING, SHIRAZ UNIVERSITY, SHIRAZ, IRAN SERAJEDDIN KATEBI DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING, SHIRAZ UNIVERSITY, SHIRAZ, IRAN Journal Article 2005 09 10 Designing an effective criterion for selecting the best rule is a major problem in the process of implementing Fuzzy Learning Classifier (FLC) systems. Conventionally confidence and support or combined measures of these are used as criteria for fuzzy rule evaluation. In this paper new entities namely precision and recall from the field of Information Retrieval (IR) systems is adapted as alternative criteria for fuzzy rule evaluation. Several different combinations of precision and recall are redesigned to produce a metric measure. These newly introduced criteria are utilized as a rule selection mechanism in the method of Iterative Rule Learning (IRL) of FLC. In several experiments, three standard datasets are used to compare and contrast the novel IR based criteria with other previously developed measures. Experimental results illustrate the effectiveness of the proposed techniques in terms of classification performance and computational efficiency.

https://ijfs.usb.ac.ir/article_439_f7e8f096de34f37fde594f63919275d0.pdf