eng
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1735-0654
2004-04-26
1
1
0
10.22111/ijfs.2004.3129
3129
Cover Vol.1, No.1, April 2004
http://ijfs.usb.ac.ir/article_3129_00f70a56cdf3e03e655abe6050b0f566.pdf
eng
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1735-0654
2004-04-22
1
1
1
3
10.22111/ijfs.2004.487
487
سایر
PREFACE
http://ijfs.usb.ac.ir/article_487_8f3746e99d2165478eba2537431bb035.pdf
eng
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1735-0654
2004-04-22
1
1
5
15
10.22111/ijfs.2004.489
489
مقاله پژوهشی
A NEURO-FUZZY GRAPHIC OBJECT CLASSIFIER WITH MODIFIED DISTANCE MEASURE ESTIMATOR
R. A. ALIEV
raliev@iatp.az
1
B. G. GUIRIMOV
guirimov@hotmail.com
2
R. R. ALIEV
rashad.aliyev@emu.edu.tr
3
MEMBER IEEE, DEPARTMENT OF COMPUTER-AIDED CONTROL SYSTEMS, AZERBAIJAN STATE OIL ACADEMY, BAKU, AZERBAIJAN
DEPARTMENT OF COMPUTER-AIDED CONTROL SYSTEMS, AZERBAIJAN STATE OIL ACADEMY, BAKU, AZERBAIJAN
EASTERN MEDITERRANEAN UNIVERSITY, NORTH CYPRUS
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.
http://ijfs.usb.ac.ir/article_489_5c2f7b44175e51dfaada2306fe314cb4.pdf
Neuro-Fuzzy technology
fuzzy logic
IF-THEN rules
Neural Network
eng
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1735-0654
2004-04-22
1
1
17
31
10.22111/ijfs.2004.491
491
مقاله پژوهشی
AN AGGREGATED FUZZY RELIABILITY INDEX FOR SLOPE STABILITY ANALYSIS
MEHRASHK MEIDANI
mehrashk@yahoo.com
1
GHASSEM HABIBAGAHI
habibg@shirazu.ac.ir
2
SERAJEDIN KATEBI
shiraz
3
MEHRASHK MEIDANI, PHD STUDENT, CIVIL ENGINEERING DEPARTMENT, SCHOOL OF ENGINEERING, SHIRAZ UNIVERSITY, SHIRAZ, IRAN
ASSOCIATE PROF., CIVIL ENGINEERING DEPARTMENT, SCHOOL OF ENGINEERING, SHIRAZ UNIVERSITY, SHIRAZ, IRAN
DEPARTMENT OF COMPUTER SCIENCE, SCHOOL OF ENGINEERING, SHIRAZ UNIVERSITY, SHIRAZ, IRAN
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.
http://ijfs.usb.ac.ir/article_491_9526bff9ac2d7524b462c937acda918a.pdf
Slope Stability
Uncertainty
Fuzzy sets
Reliability
eng
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1735-0654
2004-04-22
1
1
33
42
10.22111/ijfs.2004.492
492
مقاله پژوهشی
ON A LOSSY IMAGE COMPRESSION/RECONSTRUCTION METHOD BASED ON FUZZY RELATIONAL EQUATIONS
Kaoru Hirota
hirota@hrt.dis.titech.ac.jp
1
Hajime Nobuhara
nobuhara@hrt.dis.titech.ac.jp
2
Kazuhiko Kawamoto
kawa@hrt.dis.titech.ac.jp
3
Shin-ichi Yoshida
shin@hrt.dis.titech.ac.jp
4
Kaoru Hirota, Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, 226-8502, Japan
Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, 226-8502, Japan
Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, 226-8502, Japan
Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, 226-8502, Japan
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.
http://ijfs.usb.ac.ir/article_492_8bdd764324150deeaa297f1c8f750e1f.pdf
Fuzzy relation
Fuzzy Relational Equation
Lossy Image Compression/
Reconstruction
Ordered Structure
eng
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1735-0654
2004-04-22
1
1
43
56
10.22111/ijfs.2004.493
493
مقاله پژوهشی
FUZZY INFORMATION AND STOCHASTICS
Reinhard Viertl
r.viertl@tuwien.ac.at
1
Dietmar Hareter
hareter@statistik.tuwien.ac.at
2
Department of Statistics and Probability Theory, Vienna University of Technology, Wien, Austria
Department of Statistics and Probability Theory, Vienna University of Technology, Wien, Austria
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.
http://ijfs.usb.ac.ir/article_493_6a6242f67f83f5211eaa5d045907a805.pdf
Fuzzy numbers
Fuzzy Probability Distributions
Fuzzy Random
Variables
Fuzzy Stochastic Processes
Decision on Fuzzy Information
eng
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1735-0654
2004-04-22
1
1
57
64
10.22111/ijfs.2004.494
494
مقاله پژوهشی
ON DEGREES OF END NODES AND CUT NODES IN FUZZY GRAPHS
Kiran R. Bhutani
bhutani@cua.edu
1
John Mordeson
mordes@creighton.edu
2
Azriel Rosenfeld
ar@cfar.umd.edu
3
Department of Mathematics, The Catholic University of America, Washington, DC 20064, USA
Department of Mathematics and Computer Science, Creighton University, Omaha, NB 68178, USA
Center for Automation Research, University of Maryland, College Park, MD 20742, USA
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.
http://ijfs.usb.ac.ir/article_494_98261252c0f8ec2b92c8e8df71a38b49.pdf
Fuzzy graph
Fuzzy End Node
Strong Arc
Fuzzy Cut Node
Weak
Cut Node
eng
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1735-0654
2004-04-22
1
1
65
77
10.22111/ijfs.2004.495
495
مقاله پژوهشی
INTUITIONISTIC FUZZY HYPER BCK-IDEALS OF HYPER BCK-ALGEBRAS
Rajab Ali Borzooei
1
Young Bae Jun
ybjun@nongae.gsnu.ac.kr
2
Department of Mathematics, University of Sistan and Baluchestan, Zahedan, Iran
Department of Mathematics Education, Gyeongsang National University, Chinju (Jinju) 660-701, Korea
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.
http://ijfs.usb.ac.ir/article_495_d41d8cd98f00b204e9800998ecf8427e.pdf
Hyper BCK-algebra
inf-sup property
Intuitionistic Fuzzy (Weak
s-weak
Strong) Hyper BCK-ideal
eng
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1735-0654
2004-04-22
1
1
79
88
10.22111/ijfs.2004.496
496
مقاله پژوهشی
COUNTABLE COMPACTNESS AND THE LINDEL¨OF PROPERTY OF L-FUZZY SETS
Fu-Gui Shi
fuguishi@bit.edu.cn or f.g.shi@263.net
1
Department of Mathematics, Beijing Institute of Technology, Beijing, 100081, P.R. China
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.
http://ijfs.usb.ac.ir/article_496_53f0ab35e1d41514a4ef3a891ff5033d.pdf
L-topology
Fuzzy Compactness
Countable Compactness
Lindel¨of
Property
eng
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
1735-0654
2004-04-29
1
1
91
97
10.22111/ijfs.2004.3130
3130
Persian-translation Vol.1, No.1
http://ijfs.usb.ac.ir/article_3130_5f9da27784144f3bc7107b0714462454.pdf