University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
10
2
2013
04
29
Cover Special Issue vol. 10, no. 2, April 2013
0
EN
10.22111/ijfs.2013.2719
http://ijfs.usb.ac.ir/article_2719.html
http://ijfs.usb.ac.ir/article_2719_40548fa8cb311bf7e87b5cb4defb8845.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
10
2
2013
04
30
RANDOM FUZZY SETS: A MATHEMATICAL TOOL TO
DEVELOP STATISTICAL FUZZY DATA ANALYSIS
1
28
EN
A.
Blanco-Fernandez
Departamento de Estadstica e I.O. y D.M., Universidad de
Oviedo, Spain
blancoangela@uniovi.es
M. R.
Casals
Departamento de Estadstica e I.O. y D.M., Universidad de Oviedo,
Spain
rmcasals@uniovi.es
A.
Colubi
Departamento de Estadstica e I.O. y D.M., Universidad de Oviedo, Spain
colubi@uniovi.es
N.
Corral
Departamento de Estadstica e I.O. y D.M., Universidad de Oviedo, Spain
norbert@uniovi.es
M.
Garca-Barzana
Departamento de Estadstica e I.O. y D.M., Universidad de Oviedo,
Spain
martagb5@gmail.com
M. A.
Gil
Departamento de Estadstica e I.O. y D.M., Universidad de Oviedo, Spain
magil@uniovi.es
G.
Gonzalez-Rodrguez
Departamento de Estadstica e I.O. y D.M., Universidad de
Oviedo, Spain
gil@uniovi.es
M.T.
Lopez
Departamento de Estadstica e I.O. y D.M., Universidad de Oviedo, Spain
mtlopez@uniovi.es
M.
Montenegro
Departamento de Estadstica e I.O. y D.M., Universidad de Oviedo,
Spain
mmontenegro@uniovi.es
M. A.
Lubiano
Departamento de Estadstica e I.O. y D.M., Universidad de Oviedo,
Spain
lubiano@uniovi.es
A. B.
Ramos-Guajardo
Departamento de Estadstica e I.O. y D.M., Universidad de
Oviedo, Spain
ramosana@uniovi.es
S.
de la Rosa de Saa
Departamento de Estadstica e I.O. y D.M., Universidad de
Oviedo, Spain
delarosasara@uniovi.es
B.
Sinova
Departamento de Estadstica e I.O. y D.M., Universidad de Oviedo, Spain
sinovabeatriz@uniovi.es
10.22111/ijfs.2013.609
Data obtained in association with many real-life random experiments from different fields cannot be perfectly/exactly quantified.hspace{.1cm}Often the underlying imprecision can be suitably described in terms of fuzzy numbers/\values. For these random experiments, the scale of fuzzy numbers/values enables to capture more variability and subjectivity than that of categorical data, and more accuracy and expressiveness than that of numerical/vectorial data. On the other hand, random fuzzy numbers/sets model the random mechanisms generating experimental fuzzy data, and they are soundly formalized within the probabilistic setting.This paper aims to review a significant part of the recent literature concerning the statistical data analysis with fuzzy data and being developed around the concept of random fuzzy numbers/sets.
Distances between fuzzy numbers/values,Fuzzy numbers/values,Fuzzy arithmetic,Random fuzzy numbers/sets,Statistical methodology
http://ijfs.usb.ac.ir/article_609.html
http://ijfs.usb.ac.ir/article_609_5b8567703d17bcd661b10543f43ed47a.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
10
2
2013
04
29
AGE REPLACEMENT POLICY IN UNCERTAIN
ENVIRONMENT
29
39
EN
Kai
Yao
Department of Mathematical Sciences, Tsinghua University, Beijing 100084,
China
yaok09@mails.tsinghua.edu.cn
Dan A.
Ralescu
Department of Mathematical Sciences, University of Cincinnati,
Cincinnati, OH 45221-0025, USA
ralescd@ucmail.uc.edu
10.22111/ijfs.2013.610
Age replacement policy is concerned with finding an optional time tominimize the cost, at which time the unit is replaced even if itdoes not fail. So far, age replacement policy involving random agehas been proposed. This paper will assume the age of the unit is anuncertain variable, and find the optimal time to replace the unit.
Uncertainty theory,Renewal process,Age replacement,Maintenance
http://ijfs.usb.ac.ir/article_610.html
http://ijfs.usb.ac.ir/article_610_ee7d15bd6bca31096c32766a55373e15.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
10
2
2013
04
29
REGION MERGING STRATEGY FOR BRAIN MRI
SEGMENTATION USING DEMPSTER-SHAFER THEORY
49
56
EN
Jamal
Ghasemi
Faculty of Engineering and Technology, University of Mazan-
daran, Babolsar, Iran
j.ghasemi@umz.ac.ir
Mohamad Reza
Karami Mollaei
Faculty of Electrical and Computer Engeniering,
Babol University of Technology, P.O.Box 484, Babol, Iran
mkarami@nit.ac.ir
Reza
Ghaderi
Shahid Beheshti University, Tehran, Iran
r_ghaderi@sbu.ac.ir
Ali Hojjatoleslami
Hojjatoleslami
School of computing, University of Kent, Canterbury,CT2 7PT
UK
s.a.hojjatoleslami@kent.ac.uk
10.22111/ijfs.2013.611
Detection of brain tissues using magnetic resonance imaging (MRI) is an active and challenging research area in computational neuroscience. Brain MRI artifacts lead to an uncertainty in pixel values. Therefore, brain MRI segmentation is a complicated concern which is tackled by a novel data fusion approach. The proposed algorithm has two main steps. In the first step the brain MRI is divided to some main and ancillary cluster which is done using Fuzzy c-mean (FCM). In the second step, the considering ancillary clusters are merged with main clusters employing Dempster-Shafer Theory. The proposed method was validated on simulated brain images from the commonly used BrainWeb dataset. The results of the proposed method are evaluated by using Dice and Tanimoto coefficients which demonstrate well performance and robustness of this algorithm.
MRI,Fuzzy c-mean,Brain MRI Segmentation,Dempster-Shafer Theory
http://ijfs.usb.ac.ir/article_611.html
http://ijfs.usb.ac.ir/article_611_816e9129fa7cd7f854cbf6ff7d8fd94a.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
10
2
2013
04
29
An Empirical Comparison between Grade of Membership and Principal Component Analysis
57
72
EN
Abdul
Suleman
Department of Quantitative Methods, Instituto Universitario de
Lisboa (ISCTE - IUL), BRU-UNIDE, Av. Forcas Armadas, Lisbon, Portugal
abdul.suleman@iscte.pt
10.22111/ijfs.2013.612
t is the purpose of this paper to contribute to the discussion initiated byWachter about the parallelism between principal component (PC) and atypological grade of membership (GoM) analysis. The author testedempirically the close relationship between both analysis in a lowdimensional framework comprising up to nine dichotomous variables and twotypologies. Our contribution to the subject is also empirical. It relies ona dataset from a survey which was especially designed to study the reward ofskills in the banking sector in Portugal. The statistical data comprisethirty polythomous variables and were decomposed in four typologies using anoptimality criterion. The empirical evidence shows a high correlationbetween the first PC scores and individual GoM scores. No correlation withthe remaining PCs was found, however. In addtion to that, the first PC alsoproved effective to rank individuals by skill following the particularity ofdata distribution meanwhile unveiled in GoM analysis.
Grade of Membership,Principal component analysis,Fuzzy partition
http://ijfs.usb.ac.ir/article_612.html
http://ijfs.usb.ac.ir/article_612_196563263ef0f06cfe8860854949d512.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
10
2
2013
04
29
HURST EXPONENTS FOR NON-PRECISE DATA
73
81
EN
Mayer
Alvo
Department of Mathematics & Statistics, University of Ottawa, 585
King Edward, Ottawa, ON (K1N 5N1), Canada
malvo@uottawa.ca
Francois
Theberge
Department of Mathematics & Statistics, University of Ottawa,
585 King Edward, Ottawa, ON (K1N 5N1), Canada
ftheberg@uottawa.ca
10.22111/ijfs.2013.613
We provide a framework for the study of statistical quantitiesrelated to the Hurst phenomenon when the data are non-precise with boundedsupport.
Hurst phenomenon,Non-precise data
http://ijfs.usb.ac.ir/article_613.html
http://ijfs.usb.ac.ir/article_613_f0dcaa881ca1e193a0d1c159b2545eee.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
10
2
2013
04
29
ADAPTIVE ORDERED WEIGHTED AVERAGING FOR
ANOMALY DETECTION IN CLUSTER-BASED
MOBILE AD HOC NETWORKS
83
109
EN
Mohammad
Rahmanimanesh
Department of Electrical and Computer Engineering,
Tarbiat Modares University, Tehran, Islamic Republic of Iran
rahmanimanesh@modares.ac.ir
Saeed
Jalili
Department of Electrical and Computer Engineering, Tarbiat Modares
University, Tehran, Islamic Republic of Iran
sjalili@modares.ac.ir
10.22111/ijfs.2013.614
In this paper, an anomaly detection method in cluster-based mobile ad hoc networks with ad hoc on demand distance vector (AODV) routing protocol is proposed. In the method, the required features for describing the normal behavior of AODV are defined via step by step analysis of AODV and independent of any attack. In order to learn the normal behavior of AODV, a fuzzy averaging method is used for combining one-class support vector machine (OCSVM), mixture of Gaussians (MoG), and self-organizing maps (SOM) one-class classifiers and the combined model is utilized to partially detect the attacks in cluster members. The votes of cluster members are periodically transmitted to the cluster head and final decision on attack detection is carried out in the cluster head. In the proposed method, an adaptive ordered weighted averaging (OWA) operator is used for aggregating the votes of cluster members in the cluster head. Since the network topology, traffic, and environmental conditions of a MANET as well as the number of nodes in each cluster dynamically change, the mere use of a fixed quantifier-based weight generation approach for OWA operator is not efficient. We propose a condition-based weight generation method for OWA operator in which the number of cluster members that participate in decision making may be varying in time and OWA weights are calculated periodically and dynamically based on the conditions of the network. Simulation results demonstrate the effectiveness of the proposed method in detecting rushing, RouteError fabrication, and wormhole attacks.
Ordered weighted averaging weight generation,Mobile ad hoc network,Anomaly detection
http://ijfs.usb.ac.ir/article_614.html
http://ijfs.usb.ac.ir/article_614_8a447833fe0078753eb1c97cfe7d52f9.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
10
2
2013
04
29
Monitoring Fuzzy Capability Index $widetilde{C}_{pk}$ by Using
the EWMA Control Chart with Imprecise Data
111
132
EN
Bahram
Sadeghpour Gildeh
Faculty of Mathematical Science, Department of Sta-
tistics, University of Mazandaran, Babolsar, Iran and School of Mathematical Science,
Department of Statistics, Ferdowsi University of Mashhad, Postal Code : 9177948953,
Mashhad, Iran
sadeghpour@umz.ac.ir
Tala
Angoshtari
Faculty of Mathematical Science, Department of Statistics, Uni-
versity of Mazandaran, Babolsar, Iran
tala.angoshtari@gmail.com
10.22111/ijfs.2013.615
A manufacturing process cannot be released to production until it has been proven to be stable. Also, we cannot begin to talk about process capability until we have demonstrated stability in our process. This means that the process variation is the result of random causes only and all assignable or special causes have been removed. In complicated manufacturing processes, such as drilling process, the natural instability of the process impedes the use of any control charts for the mean and standard deviation. However, a complicated manufacturing process can be capable in spite of this natural instability.In this paper we discuss the $widetilde{C}_{pk}$ process capability index. We find the membership function of $widetilde{C}_{pk}$ based on fuzzy data. Also, by using the definition of classical control charts and the method of V$ddot{a}$nnman and Castagliola, we propose new control charts that are constructed by the $alpha$-cut sets of $widetilde{C}_{pk}$ for the natural instable manufacturing processes with fuzzy normal distributions. The results are concluded for $alpha=0.6$, that is chosen arbitrarily.
Capability index,$D_{p,q}$-distance,Fuzzy set,Membership function,EWMA control chart
http://ijfs.usb.ac.ir/article_615.html
http://ijfs.usb.ac.ir/article_615_56d575b91c8b95769c6051a0f66a4791.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
10
2
2013
04
29
ON INTERRELATIONSHIPS BETWEEN FUZZY
METRIC STRUCTURES
133
150
EN
Antonio
Roldan
Department of Statistics and Operations Research, University of
Jaen, Campus Las Lagunillas, s/n, E-23071, Jaen, Spain
afroldan@ujaen.es
Juan
Martnez-Moreno
Department of Mathematics, University of Jaen, Campus Las
Lagunillas, s/n, E-23071, Jaen, Spain
jmmoreno@ujaen.es
Concepcion
Roldan
Department of Statistics and Operations Research, University
of Granada, Campus Fuentenueva s/n, E-18071, Granada, Spain
iroldan@ugr.es
10.22111/ijfs.2013.616
Considering the increasing interest in fuzzy theory and possible applications,the concept of fuzzy metric space concept has been introduced by severalauthors from different perspectives. This paper interprets the theory in termsof metrics evaluated on fuzzy numbers and defines a strong Hausdorff topology.We study interrelationships between this theory and other fuzzy theories suchas intuitionistic fuzzy metric spaces, Kramosil and Michalek's spaces, Kalevaand Seikkala's spaces, probabilistic metric spaces, probabilisticmetric co-spaces, Menger spaces and intuitionistic probabilistic metricspaces, determining their position in the framework of theses different theories.
Fuzzy metric,Fuzzy metric space,Fuzzy number,Fuzzy topology,Links between dierent models
http://ijfs.usb.ac.ir/article_616.html
http://ijfs.usb.ac.ir/article_616_cf1477dfb706555ef5cc5a5ccacc6742.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
10
2
2013
04
29
Persian-translation Special Issue vol. 10, no. 2, April 2013
153
160
EN
10.22111/ijfs.2013.2720
http://ijfs.usb.ac.ir/article_2720.html
http://ijfs.usb.ac.ir/article_2720_c55cc2472dcccd04ee4bbbc841400cfd.pdf