University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1
1
2004
04
26
Cover Vol.1, No.1, April 2004
0
EN
10.22111/ijfs.2004.3129
http://ijfs.usb.ac.ir/article_3129.html
http://ijfs.usb.ac.ir/article_3129_00f70a56cdf3e03e655abe6050b0f566.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1
1
2004
04
22
PREFACE
1
3
EN
10.22111/ijfs.2004.487
http://ijfs.usb.ac.ir/article_487.html
http://ijfs.usb.ac.ir/article_487_8f3746e99d2165478eba2537431bb035.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1
1
2004
04
22
A NEURO-FUZZY GRAPHIC OBJECT CLASSIFIER WITH MODIFIED DISTANCE MEASURE ESTIMATOR
5
15
EN
R. A.
ALIEV
MEMBER IEEE, DEPARTMENT OF COMPUTER-AIDED CONTROL SYSTEMS, AZERBAIJAN
STATE OIL ACADEMY, BAKU, AZERBAIJAN
raliev@iatp.az
B. G.
GUIRIMOV
DEPARTMENT OF COMPUTER-AIDED CONTROL SYSTEMS, AZERBAIJAN STATE OIL
ACADEMY, BAKU, AZERBAIJAN
guirimov@hotmail.com
R. R.
ALIEV
EASTERN MEDITERRANEAN UNIVERSITY, NORTH CYPRUS
rashad.aliyev@emu.edu.tr
10.22111/ijfs.2004.489
The paper analyses issues leading to errors in graphic object classifiers. Thedistance measures suggested in literature and used as a basis in traditional, fuzzy, andNeuro-Fuzzy classifiers are found to be not suitable for classification of non-stylized orfuzzy objects in which the features of classes are much more difficult to recognize becauseof significant uncertainties in their location and gray-levels. The authors suggest a neurofuzzygraphic object classifier with modified distance measure that gives betterperformance indices than systems based on traditional ordinary and cumulative distancemeasures. Simulation has shown that the quality of recognition significantly improveswhen using the suggested method.
Neuro-Fuzzy technology,fuzzy logic,IF-THEN rules,neural network
http://ijfs.usb.ac.ir/article_489.html
http://ijfs.usb.ac.ir/article_489_5c2f7b44175e51dfaada2306fe314cb4.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1
1
2004
04
22
AN AGGREGATED FUZZY RELIABILITY INDEX FOR SLOPE STABILITY ANALYSIS
17
31
EN
MEHRASHK
MEIDANI
MEHRASHK MEIDANI, PHD STUDENT, CIVIL ENGINEERING DEPARTMENT, SCHOOL OF
ENGINEERING, SHIRAZ UNIVERSITY, SHIRAZ, IRAN
mehrashk@yahoo.com
GHASSEM
HABIBAGAHI
ASSOCIATE PROF., CIVIL ENGINEERING DEPARTMENT,
SCHOOL OF ENGINEERING, SHIRAZ UNIVERSITY, SHIRAZ, IRAN
habibg@shirazu.ac.ir
SERAJEDIN
KATEBI
DEPARTMENT OF COMPUTER SCIENCE, SCHOOL OF ENGINEERING,
SHIRAZ UNIVERSITY, SHIRAZ, IRAN
shiraz
10.22111/ijfs.2004.491
While sophisticated analytical methods like Morgenstern-Price or finite elementmethods are available for more realistic analysis of stability of slopes, assessment of the exactvalues of soil parameters is practically impossible. Uncertainty in the soil parameters arisesfrom two different sources: scatter in data and systematic error inherent in the estimate of soilproperties. Hence, stability of a slope should be expressed using a factor of safetyaccompanied by a reliability index.In this paper, the theory of fuzzy sets is used to deal simultaneously with the uncertain natureof soil parameters and the inaccuracy involved in the analysis. Soil parameters are definedusing suitable fuzzy sets and the uncertainty inherent in the value of factor of safety isassessed accordingly. It is believed that this approach accounts for the uncertainty in soilparameters more realistically compared to the conventional probabilistic approaches reportedin the literature. A computer program is developed that carries out the large amount ofcalculations required for evaluating the fuzzy factor of safety based on the concept of domaininterval analysis. An aggregated fuzzy reliability index (AFRI) is defined and assigned to thecalculated factor of safety. The proposed method is applied to a case study and the results arediscussed in details. Results from sensitivity analysis describe where the exploration effort orquality control should be concentrated. The advantage of the proposed method lies in its fastcalculation speed as well as its ease of data acquisition from experts’ opinion through fuzzysets.
Slope Stability,Uncertainty,Fuzzy sets,Reliability
http://ijfs.usb.ac.ir/article_491.html
http://ijfs.usb.ac.ir/article_491_9526bff9ac2d7524b462c937acda918a.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1
1
2004
04
22
ON A LOSSY IMAGE COMPRESSION/RECONSTRUCTION METHOD BASED ON FUZZY RELATIONAL EQUATIONS
33
42
EN
Kaoru
Hirota
Kaoru Hirota, Department
of Computational Intelligence and Systems Science, Tokyo Institute of Technology,
Yokohama, 226-8502, Japan
hirota@hrt.dis.titech.ac.jp
Hajime
Nobuhara
Department
of Computational Intelligence and Systems Science, Tokyo Institute of Technology,
Yokohama, 226-8502, Japan
nobuhara@hrt.dis.titech.ac.jp
Kazuhiko
Kawamoto
Department
of Computational Intelligence and Systems Science, Tokyo Institute of Technology,
Yokohama, 226-8502, Japan
kawa@hrt.dis.titech.ac.jp
Shin-ichi
Yoshida
Department
of Computational Intelligence and Systems Science, Tokyo Institute of Technology,
Yokohama, 226-8502, Japan
shin@hrt.dis.titech.ac.jp
10.22111/ijfs.2004.492
The pioneer work of image compression/reconstruction based onfuzzy relational equations (ICF) and the related works are introduced. TheICF regards an original image as a fuzzy relation by embedding the brightnesslevel into [0,1]. The compression/reconstruction of ICF correspond to thecomposition/solving inverse problem formulated on fuzzy relational equations.Optimizations of ICF can be consequently deduced based on fuzzy relationalcalculus, i.e., computation time reduction/improvement of reconstructed imagequality are correspond to a fast solving method/finding an approximatesolution of fuzzy relational equations, respectively. Through the experimentsusing test images extracted from Standard Image DataBAse (SIDBA), theeffectiveness of the ICF and its optimizations are shown.
Fuzzy relation,Fuzzy Relational Equation,Lossy Image Compression/
Reconstruction,Ordered Structure
http://ijfs.usb.ac.ir/article_492.html
http://ijfs.usb.ac.ir/article_492_8bdd764324150deeaa297f1c8f750e1f.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1
1
2004
04
22
FUZZY INFORMATION AND STOCHASTICS
43
56
EN
Reinhard
Viertl
Department of Statistics and Probability Theory, Vienna University
of Technology, Wien, Austria
r.viertl@tuwien.ac.at
Dietmar
Hareter
Department of Statistics and Probability Theory, Vienna University
of Technology, Wien, Austria
hareter@statistik.tuwien.ac.at
10.22111/ijfs.2004.493
In applications there occur different forms of uncertainty. The twomost important types are randomness (stochastic variability) and imprecision(fuzziness). In modelling, the dominating concept to describe uncertainty isusing stochastic models which are based on probability. However, fuzzinessis not stochastic in nature and therefore it is not considered in probabilisticmodels.Since many years the description and analysis of fuzziness is subject of intensiveresearch. These research activities do not only deal with the fuzziness ofobserved data, but also with imprecision of informations. Especially methodsof standard statistical analysis were generalized to the situation of fuzzy observations.The present paper contains an overview about of the presentationof fuzzy information and the generalization of some basic classical statisticalconcepts to the situation of fuzzy data.
Fuzzy numbers,Fuzzy Probability Distributions,Fuzzy Random
Variables,Fuzzy Stochastic Processes,Decision on Fuzzy Information
http://ijfs.usb.ac.ir/article_493.html
http://ijfs.usb.ac.ir/article_493_6a6242f67f83f5211eaa5d045907a805.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1
1
2004
04
22
ON DEGREES OF END NODES AND CUT NODES IN FUZZY GRAPHS
57
64
EN
Kiran R.
Bhutani
Department of Mathematics, The Catholic University of America,
Washington, DC 20064, USA
bhutani@cua.edu
John
Mordeson
Department of Mathematics and Computer Science, Creighton University,
Omaha, NB 68178, USA
mordes@creighton.edu
Azriel
Rosenfeld
Center for Automation Research, University of Maryland, College
Park, MD 20742, USA
ar@cfar.umd.edu
10.22111/ijfs.2004.494
The notion of strong arcs in a fuzzy graph was introduced byBhutani and Rosenfeld in [1] and fuzzy end nodes in the subsequent paper[2] using the concept of strong arcs. In Mordeson and Yao [7], the notion of“degrees” for concepts fuzzified from graph theory were defined and studied.In this note, we discuss degrees for fuzzy end nodes and study further someproperties of fuzzy end nodes and fuzzy cut nodes.
Fuzzy graph,Fuzzy End Node,Strong Arc,Fuzzy Cut Node,Weak
Cut Node
http://ijfs.usb.ac.ir/article_494.html
http://ijfs.usb.ac.ir/article_494_98261252c0f8ec2b92c8e8df71a38b49.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1
1
2004
04
22
INTUITIONISTIC FUZZY HYPER BCK-IDEALS OF HYPER BCK-ALGEBRAS
65
77
EN
Rajab Ali
Borzooei
Department of Mathematics, University of Sistan and Baluchestan,
Zahedan, Iran
Young Bae
Jun
Department of Mathematics Education, Gyeongsang National University,
Chinju (Jinju) 660-701, Korea
ybjun@nongae.gsnu.ac.kr
10.22111/ijfs.2004.495
The intuitionistic fuzzification of (strong, weak, s-weak) hyperBCK-ideals is introduced, and related properties are investigated. Characterizationsof an intuitionistic fuzzy hyper BCK-ideal are established. Using acollection of hyper BCK-ideals with some conditions, an intuitionistic fuzzyhyper BCK-ideal is built.
Hyper BCK-algebra,inf-sup property,Intuitionistic Fuzzy (Weak,s-weak,Strong) Hyper BCK-ideal
http://ijfs.usb.ac.ir/article_495.html
http://ijfs.usb.ac.ir/article_495_d41d8cd98f00b204e9800998ecf8427e.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1
1
2004
04
22
COUNTABLE COMPACTNESS AND THE LINDEL¨OF PROPERTY OF L-FUZZY SETS
79
88
EN
Fu-Gui
Shi
Department of Mathematics, Beijing Institute of Technology, Beijing,
100081, P.R. China
fuguishi@bit.edu.cn or f.g.shi@263.net
10.22111/ijfs.2004.496
In this paper, countable compactness and the Lindel¨of propertyare defined for L-fuzzy sets, where L is a complete de Morgan algebra. Theydon’t rely on the structure of the basis lattice L and no distributivity is requiredin L. A fuzzy compact L-set is countably compact and has the Lindel¨ofproperty. An L-set having the Lindel¨of property is countably compact if andonly if it is fuzzy compact. Many characterizations of countable compactnessand the Lindel¨of property are presented by means of open L-sets and closedL-sets when L is a completely distributive de Morgan algebra.
L-topology,Fuzzy Compactness,Countable Compactness,Lindel¨of
Property
http://ijfs.usb.ac.ir/article_496.html
http://ijfs.usb.ac.ir/article_496_53f0ab35e1d41514a4ef3a891ff5033d.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1
1
2004
04
29
Persian-translation Vol.1, No.1
91
97
EN
10.22111/ijfs.2004.3130
http://ijfs.usb.ac.ir/article_3130.html
http://ijfs.usb.ac.ir/article_3130_5f9da27784144f3bc7107b0714462454.pdf