University of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065415620181201Cover vol. 15, no. 6, December 20180436810.22111/ijfs.2018.4368ENJournal Article20190109https://ijfs.usb.ac.ir/article_4368_31a843d72a5b3a0de813d1d209b013f6.pdfUniversity of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065415620181230UNCERTAINTY DATA CREATING INTERVAL-VALUED FUZZY RELATION IN DECISION MAKING MODEL WITH GENERAL PREFERENCE STRUCTURE116436210.22111/ijfs.2018.4362ENBarbaraPekalaInterdisciplinary Centre for Computational Modelling, Faculty of
Mathematics and Natural Sciences, University of Rzeszow, Pigonia 1, 35-310 Rzeszow, PolandJournal Article20170508The paper introduces a new approach to preference structure, where from a weak preference relation derive the following relations:<br />strict preference, indifference and incomparability, which by aggregations and negations are created and examined. We decomposing a preference relation into a strict preference, an<br />indifference, and an incomparability relation.<br />This approach allows one to quantify different types of uncertainty in selecting alternatives.<br />In presented preference structure we use interval-valued fuzzy relations, which can be interpreted as a tool that may help to model in a better way imperfect information, especially under imperfectly<br />defined facts and imprecise knowledge.<br />Preference structures are of great interest nowadays because of their applications, so we propose at the end the algorithm of decision making by use new preference structure.https://ijfs.usb.ac.ir/article_4362_a37b978de46258d98f19e42adca9631d.pdfUniversity of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065415620181230INTUITIONISTIC FUZZY DIMENSIONAL ANALYSIS FOR MULTI-CRITERIA DECISION MAKING1740436310.22111/ijfs.2018.4363ENL.Perez-DomnguezDepartment of Industrial and Manufacturing Engineering, Universidad Autonoma de Ciudad Juarez, Av. del Charro, CP-32310, Ciudad Juarez,
Chih., MexicoA.Alvarado-IniestaDepartment of Industrial and Manufacturing Engineering, Universidad Autonoma de Ciudad Juarez, Av. del Charro, CP-32310, Ciudad Juarez, Chih.,
MexicoJ. L.Garca-AlcarazDepartment of Industrial and Manufacturing Engineering, Universidad Autonoma de Ciudad Juarez, Av. del Charro, CP-32310, Ciudad Juarez,
Chih., MexicoD. J.Valles-RosalesDepartment of Industrial and Manufacturing Engineering, New Mexico State University, Las Cruces, NM,88003-8001, USAJournal Article20170109Dimensional analysis, for multi-criteria decision making, is a mathematical method that includes diverse heterogeneous criteria into a single dimensionless index. Dimensional Analysis, in its current definition, presents the drawback to manipulate fuzzy information commonly presented in a multi-criteria decision making problem. To overcome such limitation, we propose two dimensional analysis based techniques under intuitionistic fuzzy environments. By the arithmetic operations of intuitionistic fuzzy numbers, we describe the intuitionistic fuzzy dimensional analysis (IFDA) and the aggregated intuitionistic fuzzy dimensional (AIFDA) techniques. In the first technique, we consider only the handling of fuzzy information; and, in the second one we consider both quantitative (crisp) and qualitative (fuzzy) information typically presented together in a decision making problem. To illustrate our approaches, we present some numerical examples and perform some comparisons with other well-known techniques.https://ijfs.usb.ac.ir/article_4363_03014ed31e5247c9b346565d679613fc.pdfUniversity of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065415620181230ON CONTROLLABILITY AND OBSERVABILITY OF FUZZY CONTROL SYSTEMS4164436410.22111/ijfs.2018.4364ENRoyaMastianiDepartment of Applied Mathematics, Ferdowsi University of Mashhad, Mashhad, IranSohrabEffatiDepartment of Applied Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran and The Center of Excellence on Soft Computing and Intelligent
Information Processing (SCIIP), Ferdowsi University of Mashhad, Mashhad, IranJournal Article20160409In order to more effectively cope with the real world problems of vagueness, imprecise and subjectivity, fuzzy event systems were proposed recently. In this paper, we investigate the controllability and the observability property of two systems that one of them has fuzzy variables and the other one has fuzzy coefficients and fuzzy variables (fully fuzzy system). Also, sufficient conditions for the controllability and the observability of such systems are established. Some examples are given to substantiate the results obtained.https://ijfs.usb.ac.ir/article_4364_971ec6d58c03c8e55918015df65b3323.pdfUniversity of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065415620181230MAN-MACHINE INTERACTION SYSTEM FOR SUBJECT INDEPENDENT SIGN LANGUAGE RECOGNITION USING FUZZY HIDDEN MARKOV MODEL6582436510.22111/ijfs.2018.4365ENSaad M.DarwishDepartment of Information Technology, Institute of Graduate
Studies and Research. Alexandria University, 163 Horreya Avenue. El Shatby 21526.
P.O. Box 832. Alexandria. EgyptJournal Article20161009Sign language recognition has spawned more and more interest in human–computer interaction society. The major challenge that SLR recognition faces now is developing methods that will scale well with increasing vocabulary size with a limited set of training data for the signer independent application. The automatic SLR based on hidden Markov models (HMMs) is very sensitive to gesture's shape information that makes the accurate parameters of the HMM not capable of characterizing the ambiguous distributions of the observations in gesture's features. This paper presents an extension of the HMMs using interval type-2 fuzzy sets (IT2FSs) to produce interval type-2 fuzzy HMMs to model uncertainties of hypothesis spaces (unknown varieties of parameters of the decision function). The benefit of this enlargement is that it can control both the randomness and fuzziness of traditional HMM mapping. Membership function (MF) of type-2 FS is three-dimensional that provides additional degrees of freedom to evaluate HMM's uncertainties. This system aspires to be a solution to the scalability problem, i.e. has real potential for application on a large vocabulary. Furthermore, it does not rely on the use of data gloves or other means as input devices, and operates in isolated signer-independent modes. Experimental results show that the interval type-2 fuzzy HMM has a comparable performance as that of the fuzzy HMM but is more robust to the gesture variation, while it retains almost the same computational complexity as that of the FHMM.https://ijfs.usb.ac.ir/article_4365_4af9a32540fd84c7acd5724ea5bb3fc6.pdfUniversity of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065415620181230THE CHAIN PROPERTIES AND LI-YORKE SENSITIVITY OF ZADEH'S EXTENSION ON THE SPACE OF UPPER SEMI-CONTINUOUS FUZZY SETS8395436710.22111/ijfs.2018.4367ENXinxingWuSchool of Sciences, Southwest Petroleum University, Chengdu, Sichuan,
610500, People's Republic of China0000-0002-2716-1416LidongWangZhuhai College of Jilin University, Zhuhai, Guangdong, 519041, Peoples Republic of ChinaJianhuaLiangSchool of Mathematical Sciences, Dalian University of Technology,
Liaoning, Dalian, 116024, People's Republic of ChinaJournal Article20160909Some characterizations on the chain recurrence, chain transitivity, chain mixing property,<br />shadowing and $h$-shadowing for Zadeh's extension are obtained. Besides, it is proved<br />that a dynamical system is spatiotemporally chaotic provided that the Zadeh's extension<br />is Li-Yorke sensitive.https://ijfs.usb.ac.ir/article_4367_44960db647e3ed51faf645a06403d878.pdfUniversity of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065415620181230FUZZY LOGISTIC REGRESSION BASED ON LEAST SQUARE APPROACH AND TRAPEZOIDAL MEMBERSHIP FUNCTION97106436910.22111/ijfs.2018.4369ENSaimaMustafaDepartment of Mathematics and Statistics, Pir Mehr Ali Shah Arid
Agriculture University, Rawalpindi, PakistanSobiaAsgharDepartment of Mathematics and Statistics, Pir Mehr Ali Shah Arid
Agriculture University, Rawalpindi, PakistanMuhammadHanifDepartment of Mathematics and Statistics, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, PakistanJournal Article20151209Logistic regression is a non-linear modification of the linear<br />regression. The purpose of the logistic regression analysis is to<br />measure the effects of multiple explanatory variables which can be<br />continuous and response variable is categorical. In real life there are<br />situations which we deal with information that is vague in<br />nature and there are cases that are not explained<br />precisely. In this regard, we have used the concept of possiblistic<br />odds and fuzzy approach. Fuzzy logic deals with linguistic<br />uncertainties and extracting valuable information from linguistic<br />terms. In our study, we have developed fuzzy possiblistic logistic<br />model with trapezoidal membership function and fuzzy possiblistic<br />logistic model is a tool that help us to deal with imprecise<br />observations. Comparison fuzzy logistic regression model with classical<br />logistic regression has been done by goodness of fit criteria on real life as an example.https://ijfs.usb.ac.ir/article_4369_94828c9667653b8813d75082ee62a77d.pdfUniversity of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065415620181230INCOMPLETE INTERVAL-VALUED HESITANT FUZZY PREFERENCE RELATIONS IN DECISION MAKING107120437010.22111/ijfs.2018.4370ENA.KhalidLahore School of Economics, Centre for Mathematics and Statistical
Sciences, Lahore, PaksitanIsmatBegCentre for Mathematics and Statistical Sciences, Lahore School of Economics, Lahore, 54000, PakistanJournal Article20161009In this article, we propose a method to deal with incomplete interval-valued<br />hesitant fuzzy preference relations. For this purpose, an additive<br />transitivity inspired technique for interval-valued hesitant fuzzy<br />preference relations is formulated which assists in estimating missing<br />preferences. First of all, we introduce a condition for decision makers<br />providing incomplete information. Decision makers expressing incomplete data<br />are expected to abide by the proposed condition. This ensures that the<br />estimated preferences are well-defined intervals which otherwise may not be<br />possible. Additionally, this condition eliminates the problem of outlying<br />estimated preferences. After resolving the issue of incompleteness, this<br />article proposes a ranking rule for reciprocal and non-reciprocal<br />interval-valued hesitant fuzzy preference relations.https://ijfs.usb.ac.ir/article_4370_ce65f01ee9b6f2dd77ab1fac4c129a53.pdfUniversity of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065415620181230SINGLE MACHINE DUE DATE ASSIGNMENT SCHEDULING PROBLEM WITH PRECEDENCE CONSTRAINTS AND CONTROLLABLE PROCESSING TIMES IN FUZZY ENVIRONMENT121143437110.22111/ijfs.2018.4371ENJinquanLiSchool of Applied Mathematics, Zhuhai Municipal Key Laboratory of
Intelligent Control, Beijing Normal University Zhuhai, Zhuhai 519087, P.R. ChinaDehuaXuSchool of Science, East China University of Technology, Nanchang
330013, P.R. ChinaHongxingLiSchool of Electronic and Information Engineering, Dalian University
of Technology, Dalian 116024, P.R. ChinaJournal Article20170509In this paper, a due date assignment scheduling problem with precedence constraints and controllable processing times<br /> in uncertain environment is investigated, in which the basic processing time of each job is assumed to be the symmetric trapezoidal fuzzy number, and the linear resource consumption function is used.<br />The objective is to minimize the crisp possibilistic mean (or expected) value of a cost function that<br />includes the costs of earliness, tardiness, makespan and resource consumption jointly by scheduling the jobs under precedence constraints and determining the due date and the resource allocation amount<br /> satisfying resource constraints for each job. First, the problem is shown to be NP-hard. Furthermore, an optimal algorithm with polynomial time for the special case of this problem is put forward. Moreover,<br /> an efficient 2-approximation algorithm is presented based on solving the relaxation of the problem. Finally, the numerical experiment is given, whose results show that our method is promising.https://ijfs.usb.ac.ir/article_4371_f8b14d686fb69693683e2e6e095379da.pdfUniversity of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065415620181230ON TOPOLOGICAL EQ-ALGEBRAS145158437210.22111/ijfs.2018.4372ENJiangYangSchool of Mathematics, Northwest University, Xi'an,710127, ChinaXiao LongXinSchool of Mathematics, Northwest University, Xi'an,710127, ChinaPeng FeiHeSchool of Mathematics and Information Science, Shaanxi Normal University, Xi'an, 710119, ChinaJournal Article20161209In this paper, by using a special family of filters $\mathcal{F}$ on an EQ-algebra $E$, we construct a topology $\mathcal{T}_{\mathcal{\mathcal{F}}}$ on $E$ and show that $(E,\mathcal{T}_{\mathcal{F}})$ is a topological EQ-algebra. First of all, we give some properties of topological EQ-algebras and investigate the interaction of topological EQ-algebras and quotient topological EQ-algebras. Then we obtain the form of closure of each subset and show that $(E,\mathcal{T}_{\mathcal{F}})$ is a zero-dimensional space. Finally, we introduce the concept of convergence of sequences on topological EQ-algebras and give a condition under which the limit of a sequence is unique.https://ijfs.usb.ac.ir/article_4372_dc83354d4e54be047e1c1cb9ea76b978.pdfUniversity of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065415620181230M-FUZZIFYING TOPOLOGICAL CONVEX SPACES159174437310.22111/ijfs.2018.4373ENKaiWangSchool of Mathematics and statistics, Beijing Institute of Technology,
Beijing 100081, P.R. ChinaFu-GuiShiSchool of Mathematics and statistics, Beijing Institute of Technology,
Beijing 100081, P.R. ChinaJournal Article20170409The main purpose of this paper is to introduce the compatibility of $M$-fuzzifying topologies and $M$-fuzzifying convexities, define an $M$-fuzzifying topological convex space, and give a method to generate an $M$-fuzzifying topological convex space. Some characterizations of $M$-fuzzifying topological convex spaces are presented. Finally, the notion of $M$-fuzzifying weak topologies is obtained from $M$-fuzzifying topological convex spaces.https://ijfs.usb.ac.ir/article_4373_e339b7cb16c558465e67f0154f15b7cd.pdfUniversity of Sistan and BaluchestanIranian Journal of Fuzzy Systems1735-065415620181230Persian-translation vol. 15, no. 6, December 2018177194437410.22111/ijfs.2018.4374ENJournal Article20190109https://ijfs.usb.ac.ir/article_4374_396458c17d03a27a80381e7563d91360.pdf