ORIGINAL_ARTICLE
Cover Vol.1, No.1, April 2004
http://ijfs.usb.ac.ir/article_3129_00f70a56cdf3e03e655abe6050b0f566.pdf
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10.22111/ijfs.2004.3129
ORIGINAL_ARTICLE
PREFACE
http://ijfs.usb.ac.ir/article_487_8f3746e99d2165478eba2537431bb035.pdf
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10.22111/ijfs.2004.487
ORIGINAL_ARTICLE
A NEURO-FUZZY GRAPHIC OBJECT CLASSIFIER WITH MODIFIED DISTANCE MEASURE ESTIMATOR
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
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15
10.22111/ijfs.2004.489
Neuro-Fuzzy technology
Fuzzy logic
IF-THEN rules
Neural Network
R. A.
ALIEV
raliev@iatp.az
true
1
MEMBER IEEE, DEPARTMENT OF COMPUTER-AIDED CONTROL SYSTEMS, AZERBAIJAN
STATE OIL ACADEMY, BAKU, AZERBAIJAN
MEMBER IEEE, DEPARTMENT OF COMPUTER-AIDED CONTROL SYSTEMS, AZERBAIJAN
STATE OIL ACADEMY, BAKU, AZERBAIJAN
MEMBER IEEE, DEPARTMENT OF COMPUTER-AIDED CONTROL SYSTEMS, AZERBAIJAN
STATE OIL ACADEMY, BAKU, AZERBAIJAN
LEAD_AUTHOR
B. G.
GUIRIMOV
guirimov@hotmail.com
true
2
DEPARTMENT OF COMPUTER-AIDED CONTROL SYSTEMS, AZERBAIJAN STATE OIL
ACADEMY, BAKU, AZERBAIJAN
DEPARTMENT OF COMPUTER-AIDED CONTROL SYSTEMS, AZERBAIJAN STATE OIL
ACADEMY, BAKU, AZERBAIJAN
DEPARTMENT OF COMPUTER-AIDED CONTROL SYSTEMS, AZERBAIJAN STATE OIL
ACADEMY, BAKU, AZERBAIJAN
AUTHOR
R. R.
ALIEV
rashad.aliyev@emu.edu.tr
true
3
EASTERN MEDITERRANEAN UNIVERSITY, NORTH CYPRUS
EASTERN MEDITERRANEAN UNIVERSITY, NORTH CYPRUS
EASTERN MEDITERRANEAN UNIVERSITY, NORTH CYPRUS
AUTHOR
[1] J. Bezdek and S. Pal (ed.), Fuzzy models for pattern recognition, New York: IEEE Press (1992).
1
[2] X. Ye, C.Suen and M.Cheriet, A generic system to extract and clean handwritten data from business
2
forms, in Prof. Int. Workshop on Frontiers in handwriting Recognition, Amsterdam (2000) 63-72.
3
[3] R. A. Aliev and R. R. Aliev, Soft Computing and its applications, World Scientific Publishing Co.
4
Pte. Ltd (2001) p. 444.
5
[4] K. Saastamoinen, V. Könönen, and P. Luukka, A classifier based on the fuzzy similarity in the
6
Lukasiewicz structure with different metrics, in proceedings of IEEE International Conference on
7
Fuzzy Systems, FUZZ-IEEE’02, Vol. 1 (2002) 363-367.
8
[5] H. Bandemer and W. Näther, Fuzzy data analysis, Theory and Decision Library, Series B:
9
Mathematical and Statistical Methods, Vol. 20, Cluwer Academic Publishers (1992) 67-71.
10
[6] L. Mascarilla and C. Frélicot, Combining rejection-based pattern classifiers, in 19th International
11
Conference of the North American Fuzzy Information Processing Society – NAFIPS, PeachFuzz
12
2000 (2000) 114-118.
13
[7] R Aliev and B. Guirimov, Handwritten image recognition by using neural and fuzzy approaches,
14
Intelligent Control and Decision Making Systems, No. 1, Thematic Collected Articles, Baku,
15
Publishing House of Azerb. State Oil Academy (1997) 3-7.
16
[8] R. Aliev, B. Guirimov, K. Bonfig, and Steinmann, A neuro-fuzzy algorithm for recognition of nonstylized
17
images, in proceedings of Fourth International Conference on Application of Fuzzy
18
Systems and Soft Computing, ICAFS’2000, Siegen, Germany (2000) 238-241.
19
[9] S. Halgamuge and M. Glesner, A fuzzy neural approach for pattern classification with generation of
20
rules based on supervised learning, in proceedings of Nuro Nimes 92 (1992) 165-173.
21
[10] V. Uebele, S. Abe, and M. Lan, A neural - network based fuzzy classifier, IEEE Transactions on
22
Systems, Man, and Cybernetics, Vol. 23, No. 3 (1995) 353-361.
23
[11] R. Yager, A general approach to rule aggregation in fuzzy logic control, Appl. Intelligence, 2
24
(1992) 333-351. [12] R. Yager, On a general class of fuzzy connectives, Fuzzy Sets and Systems, 4 (1980) 235-242.
25
ORIGINAL_ARTICLE
AN AGGREGATED FUZZY RELIABILITY INDEX FOR SLOPE STABILITY ANALYSIS
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
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10.22111/ijfs.2004.491
Slope Stability
Uncertainty
Fuzzy sets
Reliability
MEHRASHK
MEIDANI
mehrashk@yahoo.com
true
1
MEHRASHK MEIDANI, PHD STUDENT, CIVIL ENGINEERING DEPARTMENT, SCHOOL OF
ENGINEERING, SHIRAZ UNIVERSITY, SHIRAZ, IRAN
MEHRASHK MEIDANI, PHD STUDENT, CIVIL ENGINEERING DEPARTMENT, SCHOOL OF
ENGINEERING, SHIRAZ UNIVERSITY, SHIRAZ, IRAN
MEHRASHK MEIDANI, PHD STUDENT, CIVIL ENGINEERING DEPARTMENT, SCHOOL OF
ENGINEERING, SHIRAZ UNIVERSITY, SHIRAZ, IRAN
AUTHOR
GHASSEM
HABIBAGAHI
habibg@shirazu.ac.ir
true
2
ASSOCIATE PROF., CIVIL ENGINEERING DEPARTMENT,
SCHOOL OF ENGINEERING, SHIRAZ UNIVERSITY, SHIRAZ, IRAN
ASSOCIATE PROF., CIVIL ENGINEERING DEPARTMENT,
SCHOOL OF ENGINEERING, SHIRAZ UNIVERSITY, SHIRAZ, IRAN
ASSOCIATE PROF., CIVIL ENGINEERING DEPARTMENT,
SCHOOL OF ENGINEERING, SHIRAZ UNIVERSITY, SHIRAZ, IRAN
AUTHOR
SERAJEDIN
KATEBI
shiraz
true
3
DEPARTMENT OF COMPUTER SCIENCE, SCHOOL OF ENGINEERING,
SHIRAZ UNIVERSITY, SHIRAZ, IRAN
DEPARTMENT OF COMPUTER SCIENCE, SCHOOL OF ENGINEERING,
SHIRAZ UNIVERSITY, SHIRAZ, IRAN
DEPARTMENT OF COMPUTER SCIENCE, SCHOOL OF ENGINEERING,
SHIRAZ UNIVERSITY, SHIRAZ, IRAN
LEAD_AUTHOR
[1] M. Andonyadis, A. G. Altschaeffl and J. L. Chameau, “Fuzzy sets in pavement evaluation and
1
management: Application and interpretation.” FHWA/IN/JHRP report #85-14, School of Civil
2
Engrg., Purdue Univ., (1985).
3
[2] B. M. Ayyub, Uncertainty Modeling and Analysis in Civil Engineering, CRC Press, Boca Raton,
4
Florida, (1998).
5
[3] E. N. Broomhead, The Stability of Slopes, Surrey Univ. Press, Chapman and Hall, New York,
6
[4] T. B. Celestino and J. M. Duncan, Simplified search for non-circular slip surface, Proc., 10th Int.
7
Conf. on Soil Mech. and Found. Engrg., A. A. Balkema, Rotterdam, The Netherlands, 3 (1981)
8
[5] R. N. Chowdhury, and D. A-Grivas, Probabilistic model of progressive failure of slopes, J.
9
Geotech. Engrg. Div., ASCE, 108(6) (1982) 803-819.
10
[6] R. N. Chowdhury and D. H. Xu, Slope system reliability with general slip surfaces, Soils and
11
Foundations, 34(3) (1994) 99-105.
12
[7] J. T. Christian, C. C. Ladd and G. B. Baecher, Reliability applied to slope stability analysis, J.
13
Geotech. Engrg., ASCE, 120(12) (1994) 2180-2207.
14
[8] J. T. Christian, C. C. Ladd and G. B. Baecher, Reliability and probability in stability analysis, Proc.,
15
Stability and Performance of Slopes and Embankments-II, ASCE, New York, N.Y., Vol. 2 (1992)
16
1071-1111.
17
[9] W. M. Dong, H. C. Shah and F. S. Wang, Fuzzy computations in risk and decision analysis, Civ.
18
Engng. Syst., Vol. 2, Dec. (1985) 201-208.
19
[10] J. M. Duncan, State of the art: limit equilibrium and finite element analysis of slopes, J. Geotech.
20
Engrg., ASCE, 122(7) (1996) 577-596.
21
[11] R. B. Gilbert, S. G. Wright and E. Liedtke, Uncertainty in back analysis of slopes: Kettleman Hills
22
case history, J. Geotech. Engrg., ASCE, 124(12) (1998) 1167-1176.
23
[12] S. Gui, R. Zhang, J. P. Turner and X. Xue, Probabilistic slope stability analysis with stochastic soil
24
hydraulic conductivity, J. Geotech. Engrg., ASCE, 126(1) (2000) 1-9.
25
[13] A. M. Hassan and T. F. Wolff, Search algorithm for minimum reliability index of earth slopes, J.
26
Geotech. Engrg., ASCE, 125(4) (1999) 301-308.
27
[14] C. H. Juang, J. L. Way and D. J. Elton, Model for capacity of single piles in sand using fuzzy sets, J.
28
Geotech. Engrg., ASCE, 117(2) (1991) 1920-1931.
29
[15] K. S. Li, and P. Lumb, Probabilistic design of slopes, Can. Geotech. J., Vol. 24 (1987) 520- 535.
30
[16] B. K. Low and W. H. Tang, Probabilistic slope analysis using Janbu’s generalized procedure of
31
slices, Computers and Geotechnics, 21(2) (1997) 121-142.
32
[17] N. R. Morgenstern and V. E. Price, The analysis of the stability of general slip surfaces,
33
Geotechnique, Vol. 15, (1965).
34
[18] T. J. Ross, Fuzzy Logic with Engineering Applications, McGraw Hill Inc., (1995).
35
[19] A. W. Skempton and D. J. Coats, Carsington dam failure, Failures in Earthworks, Proc. of the
36
symp. on Failures in Earthworks, Thomas Telford Ltd., London, (1985).
37
[20] S. Soulati, “A Gentic approach for determining the generalized interslice forces and the critical
38
noncircular slip surface for slope stability analysis”, Thesis submitted to the School of Graduate
39
studies in Partial Fulfillment of the Requirements for the Degree of Mater of Science, Shiraz
40
University, Shiraz, Iran, (2003).
41
[21] E. Spencer, Circular and logarithmic spiral slip surfaces, J. Soil Mech. And Found. Div., ASCE,
42
95(1) (1969) 929-942.
43
[22] E. Spencer, Slip circles and critical shear planes, J. Geotech. Engrg. Div., ASCE, 107(7) (1981)
44
[23] W. H. Tang, M. S. Yucemen and A. H.-S. Ang, Probability-based short term design of slopes, Can.
45
Geotech. J., 13(3) (1976) 201- 215.
46
[24] E. H. Vanmarcke, Reliability of earth slopes, J.Geotech. Engrg. Div., ASCE, 103(11) (1977) 1227-
47
[25] R. V. Whitman, Evaluating calculated risk in geotechnical engineering, J. Geotech. Engrg., ASCE,
48
110(2) (1984) 145-188.
49
[26] L. A. Zadeh, Fuzzy sets, Information and Control, 8 (1965) 338-353.
50
[27] H. J. Zimmermann, Fuzzy Set Theory and Its Applications, Kluwer Academic Publishers, (1996).
51
ORIGINAL_ARTICLE
ON A LOSSY IMAGE COMPRESSION/RECONSTRUCTION METHOD BASED ON FUZZY RELATIONAL EQUATIONS
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
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10.22111/ijfs.2004.492
Fuzzy relation
Fuzzy Relational Equation
Lossy Image Compression/
Reconstruction
Ordered Structure
Kaoru
Hirota
hirota@hrt.dis.titech.ac.jp
true
1
Kaoru Hirota, Department
of Computational Intelligence and Systems Science, Tokyo Institute of Technology,
Yokohama, 226-8502, Japan
Kaoru Hirota, Department
of Computational Intelligence and Systems Science, Tokyo Institute of Technology,
Yokohama, 226-8502, Japan
Kaoru Hirota, Department
of Computational Intelligence and Systems Science, Tokyo Institute of Technology,
Yokohama, 226-8502, Japan
AUTHOR
Hajime
Nobuhara
nobuhara@hrt.dis.titech.ac.jp
true
2
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
LEAD_AUTHOR
Kazuhiko
Kawamoto
kawa@hrt.dis.titech.ac.jp
true
3
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
AUTHOR
Shin-ichi
Yoshida
shin@hrt.dis.titech.ac.jp
true
4
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
AUTHOR
[1] A. DiNola, W. Pedrycz, and S. Sessa, Fuzzy Relational Structures: The State-of-Art, Fuzzy
1
Sets and Systems, Vol. 75, No. 3(1995) 241-262.
2
[2] A. DiNola, S. Sessa, W. Pedrycz, and E. Sanchez, Fuzzy Relation Equation and Their Applications
3
to Knowledge Engineering, Kluwer Academic Publishers, 1989.
4
[3] K. Hirota, and W. Pedrycz, Fuzzy Relational Compression, IEEE Transactions on Systems,
5
Man, and Cybernetics, Vol. 29 , No. 3(1999) 407-415.
6
[4] H. Nobuhara, W. Pedrycz, and K. Hirota, Fast Solving Method of Fuzzy Relational Equation
7
and Its Application to Lossy Image Compression/Reconstruction, IEEE Transactions on
8
Fuzzy Systems, Vol. 8, No. 3(2000) 325-334.
9
[5] H. Nobuhara, Y. Takama, and K. Hirota, Image Compression/Reconstruction Based on Various
10
Types of Fuzzy Relational Equations, The Transaction of The Institute of Electrical
11
Engineers of Japan (in Japanese), Vol. 121, No. 6 (2001) 1102-1113.
12
[6] H. Nobuhara, Y. Takama, W. Pedrycz, and K. Hirota, Lossy Image Compression and Reconstruction
13
Based on Fuzzy Relational Equations,Fuzzy Filters for Image Processing, Springer
14
(2002) 339-355.
15
[7] H. Nobuhara, W. Pedrycz, and K. Hirota, A Digital Watermarking Algorithm using Image
16
Compression Method based on Fuzzy Relational Equation, IEEE International Conference on
17
Fuzzy Systems, Hawaii, USA, May 12-17 (2002) (CD-Proceedings).
18
[8] H. Nobuhara, W. Pedrycz, and K. Hirota, Fuzzy Relational Image Compression using Nonuniform
19
Coders Designed by Overlap Level of Fuzzy Sets, International Conference on Fuzzy
20
Systems and Knowledge Discovery (FSKD’02), 2002, Singapore (CD-Proceedings).
21
[9] H. Nobuhara, and K. Hirota, Non-uniform Coders Design for Motion Compression Method by
22
Fuzzy Relational Equation, International Fuzzy System AssociationWorld Congress, Istanbul,
23
Turkey, June 29 - July 2, Lecture Notes in Artificiall Intelligence, No. 2715(2003) 428-435.
24
[10] W. Pedrycz, Fuzzy Relational Equations with Generalized Connectives and Their Applications,
25
Fuzzy Sets and Systems, Vol. 10 (1983) 185-201.
26
ORIGINAL_ARTICLE
FUZZY INFORMATION AND STOCHASTICS
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
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10.22111/ijfs.2004.493
Fuzzy numbers
Fuzzy Probability Distributions
Fuzzy Random
Variables
Fuzzy Stochastic Processes
Decision on Fuzzy Information
Reinhard
Viertl
r.viertl@tuwien.ac.at
true
1
Department of Statistics and Probability Theory, Vienna University
of Technology, Wien, Austria
Department of Statistics and Probability Theory, Vienna University
of Technology, Wien, Austria
Department of Statistics and Probability Theory, Vienna University
of Technology, Wien, Austria
AUTHOR
Dietmar
Hareter
hareter@statistik.tuwien.ac.at
true
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
Department of Statistics and Probability Theory, Vienna University
of Technology, Wien, Austria
LEAD_AUTHOR
[1] E.P. Klement, M.L. Puri, D.A. Ralescu, Law of large numbers and central limit theorem for
1
fuzzy random variables, Cybernetics and Systems Research 2, Proc. 7th Europ. Meet., Vienna
2
1984, 525-529 (1984).
3
[2] E.P. Klement, R. Mesiar, E. Pap, Triangular Norms, Kluwer Acad. Publ., Dordrecht, 2000 .
4
[3] G. Klir, B. Yuan, Fuzzy Sets and Fuzzy Logic - Theory and Applications, Prentice Hall, Upper
5
Saddle River, New Jersey, 1995 .
6
[4] R. Kruse, The strong law of large numbers for fuzzy random variables, Information Science,
7
Vol. 28 (1982) 233-241 .
8
[5] H. Kwakernaak, Fuzzy random variables - I. definition and theorems, Information Science,
9
Vol. 15 (1978) 1-29 .
10
[6] H. Kwakernaak, Fuzzy random variables - II. algorithms and examples, Information Science,
11
Vol. 17 (1979) 253-278 .
12
[7] B. M¨oller, W. Graf, M. Beer, Safety assessment of structures in view of fuzzy randomness,
13
Computers & Structures, Vol. 81 (2003).
14
[8] S. Niculescu, R. Viertl, Bernoulli’s Law of Large Numbers for Vague Data, Fuzzy Sets and
15
Systems, Vol. 50 (1992).
16
[9] M.L. Puri, D.A. Ralescu, Fuzzy Random Variables, Journal of Math. Anal. and Appl., Vol.
17
114 (1986) 409-422 .
18
[10] C. R¨omer, A. Kandel, Statistical tests for fuzzy data, Fuzzy Sets and Systems, Vol. 72 (1995).
19
[11] J. Sickert, M. Beer, W. Graf, B. M¨oller, Fuzzy probabilistic structural analysis considering
20
fuzzy random functions, in A. Kiureghian, S. Madanat, J. Pestana (Eds.), Applications of
21
Statistics and Probability in Civil Engineering, Milpress, Rotterdam, 2003 .
22
[12] S.M. Taheri, Trends in Fuzzy Statistics, Austrian Journal of Statistics, Vol. 32, No. 3 (2003)
23
[13] R. Viertl, Statistical Methods for Non-Precise Data, CRC Press, Boca Raton, Florida, 1996
24
[14] R. Viertl, On the description and analysis of measurements of continuous quantities, Kybernetika,
25
Vol. 38 (2002).
26
[15] P. Filzmoser, R. Viertl, Testing Hypotheses with Fuzzy Data, The Fuzzy p-value, to appear
27
in Metrika.
28
[16] R. Viertl, D. Hareter, Fuzzy Information and Imprecise Probability, to appear in ZAMM.
29
[17] W. Voß (Ed.), Taschenbuch der Statistik, Carl Hauser Verlag, M¨unchen, 2004.
30
[18] G. Wang, Y. Zhang, The theory of fuzzy stochastic processes, Fuzzy Sets and Systems, Vol.
31
51 (1992).
32
ORIGINAL_ARTICLE
ON DEGREES OF END NODES AND CUT NODES IN FUZZY GRAPHS
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
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10.22111/ijfs.2004.494
Fuzzy graph
Fuzzy End Node
Strong Arc
Fuzzy Cut Node
Weak
Cut Node
Kiran R.
Bhutani
bhutani@cua.edu
true
1
Department of Mathematics, The Catholic University of America,
Washington, DC 20064, USA
Department of Mathematics, The Catholic University of America,
Washington, DC 20064, USA
Department of Mathematics, The Catholic University of America,
Washington, DC 20064, USA
LEAD_AUTHOR
John
Mordeson
mordes@creighton.edu
true
2
Department of Mathematics and Computer Science, Creighton University,
Omaha, NB 68178, USA
Department of Mathematics and Computer Science, Creighton University,
Omaha, NB 68178, USA
Department of Mathematics and Computer Science, Creighton University,
Omaha, NB 68178, USA
AUTHOR
Azriel
Rosenfeld
ar@cfar.umd.edu
true
3
Center for Automation Research, University of Maryland, College
Park, MD 20742, USA
Center for Automation Research, University of Maryland, College
Park, MD 20742, USA
Center for Automation Research, University of Maryland, College
Park, MD 20742, USA
AUTHOR
[1] K.R. Bhutani and A. Rosenfeld, Strong arcs in fuzzy graphs, Information Sciences, 152 (2003)
1
[2] K.R. Bhutani, A. Rosenfeld, Fuzzy end nodes in fuzzy graphs, Information Sciences, 152
2
(2003) 323-326.
3
[3] S.F. Buckley, F. Harary, Distance in Graphs, Addison-Wesley, Redwood City, CA, 1990.
4
[4] M. Delgado, J.L Verdegay, M. A. Vila, On fuzzy tree definition, European J. Operations
5
Research, 22 (1985) 243-249.
6
[5] C.M. Klein, Fuzzy Shortest Paths, Fuzzy Sets and Systems, 39 (1991) 27-41.
7
[6] J.N. Mordeson, P.S. Nair, Fuzzy Graphs and Fuzzy Hypergraphs, Physica-Verlag, Heidelberg,
8
[7] J.N. Mordeson, Y. Y. Yao, it Fuzzy cycles and fuzzy trees, The Journal of Fuzzy Mathematics,
9
10 (2002) 189-202.
10
[8] A. Rosenfeld, Fuzzy graphs, in L.A. Zadeh, K.S.Fu, K. Tanaka, and M. Shimura, eds., Fuzzy
11
Sets and their Applications to Cognitive and Decision Processes, Academic Press, New York
12
(1975) 77–95.
13
[9] M.S. Sunitha, A. Vijayakumar, A characterization of fuzzy trees, Information Sciences, 113
14
(1999) 293-300.
15
ORIGINAL_ARTICLE
INTUITIONISTIC FUZZY HYPER BCK-IDEALS OF HYPER BCK-ALGEBRAS
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
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2018-09-18T11:23:20
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10.22111/ijfs.2004.495
Hyper BCK-algebra
inf-sup property
Intuitionistic Fuzzy (Weak
s-weak
Strong) Hyper BCK-ideal
Rajab Ali
Borzooei
true
1
Department of Mathematics, University of Sistan and Baluchestan,
Zahedan, Iran
Department of Mathematics, University of Sistan and Baluchestan,
Zahedan, Iran
Department of Mathematics, University of Sistan and Baluchestan,
Zahedan, Iran
AUTHOR
Young Bae
Jun
ybjun@nongae.gsnu.ac.kr
true
2
Department of Mathematics Education, Gyeongsang National University,
Chinju (Jinju) 660-701, Korea
Department of Mathematics Education, Gyeongsang National University,
Chinju (Jinju) 660-701, Korea
Department of Mathematics Education, Gyeongsang National University,
Chinju (Jinju) 660-701, Korea
LEAD_AUTHOR
[1] K. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20 , No. 1 (1986) 87-96.
1
[2] K. Atanassov, New operations defined over the intuitionistic fuzzy sets, Fuzzy Sets and Systems,
2
61 (1994) 137-142.
3
[3] Y. B. Jun and W. H. Shim, Fuzzy implicative hyper BCK-ideals of hyper BCK-algebras,
4
Internat. J. Math. & Math. Sci., 29 , No. 2 (2002) 63–70.
5
[4] Y. B. Jun and X. L. Xin, Scalar elements and hyperatoms of hyper BCK-algebras, Scientiae
6
Mathematicae, Vol. 2 , No. 3 (1999) 303–309.
7
[5] Y. B. Jun and X. L. Xin, Fuzzy hyper BCK-ideals of hyper BCK-algebras, Scientiae Mathematicae
8
Japonicae, Vol. 53 , No. 2 (2001) 353–360.
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[6] Y. B. Jun, X. L. Xin, E. H. Roh and M. M. Zahedi, Strong hyper BCK-ideals of hyper
10
BCK-algebras, Math. Japonica, Vol. 51 , No. 3 (2000), 493–498.
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[7] Y. B. Jun, M. M. Zahedi, X. L. Xin and R. A. Borzooei, On hyper BCK-algebras, Italian J.
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of Pure and Appl. Math., 8 (2000) 127-136.
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[8] F. Marty, Sur une generalization de la notion de groupe, 8th Congress Math. Scandinaves,
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Stockholm (1934) 45-49.
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[9] L. A. Zadeh, Fuzzy sets, Information and Control, 8 (1965) 338–353.
16
ORIGINAL_ARTICLE
COUNTABLE COMPACTNESS AND THE LINDEL¨OF PROPERTY OF L-FUZZY SETS
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
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2018-09-18T11:23:20
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10.22111/ijfs.2004.496
L-topology
Fuzzy Compactness
Countable Compactness
Lindel¨of
Property
Fu-Gui
Shi
fuguishi@bit.edu.cn or f.g.shi@263.net
true
1
Department of Mathematics, Beijing Institute of Technology, Beijing,
100081, P.R. China
Department of Mathematics, Beijing Institute of Technology, Beijing,
100081, P.R. China
Department of Mathematics, Beijing Institute of Technology, Beijing,
100081, P.R. China
AUTHOR
[1] G. Gierz, et al., A compendium of continuous lattices, Springer Verlag, Berlin, 1980.
1
[2] R. Lowen, Fuzzy topological spaces and fuzzy compactness. J. Math. Anal. Appl., 56 (1976)
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621–633.
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[3] F.-G. Shi, Fuzzy compactness in L-topological spaces, submitted.
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[4] F.-G. Shi, Characterizations of fuzzy compactness and the Tychonoff Theorem, J. Mudanjiang
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Teachers Collge (Natural Science Editor), 1 (1992) 1-3 (in Chinese).
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[5] F.-G Shi, A note on fuzzy compactness in L-topological spaces, Fuzzy Sets and Systems, 119
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(2001) 547-548.
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[6] F.-G. Shi,The Lindel¨of property in L-fuzzy topological Spaces, J.Yantai Teachers University
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(Natural Science Editor), 4 (1994) 241-244 (in Chinese).
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[7] F.-G. Shi, et al.,Fuzzy countable compactness in L-fuzzy topological spaces, J. Harbin Science
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and Technology University, 3 (1992) 63-72 (in Chinese).
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[8] F.-G. Shi, C.-Y. Zheng, Paracompactness in L-fuzzy topological spaces, Fuzzy Sets and Systems,
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129 (2002) 29-37.
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[9] F.-G. Shi, C.-Y. Zheng, O-convergence of fuzzy nets and its applications, Fuzzy Sets and
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Systems, 140 (2003) 499-507.
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[10] G.J. Wang,A new fuzzy compactness defined by fuzzy nets, J. Math. Anal. Appl., 94 (1983)
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[11] G.J. Wang, Theory of L-fuzzy topological space, Shanxi Normal University Press, Xian, 1988
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(in Chinese).
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[12] G.-J. Wang, Theory of topological molecular lattices, Fuzzy Sets and Systems, 47 (1992)
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[13] J.J. Xu, On fuzzy compactness in L-fuzzy topological spaces, Chinese Quarterly Journal of
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Mathematics, 2 (1990) 104-105.
22
ORIGINAL_ARTICLE
Persian-translation Vol.1, No.1
http://ijfs.usb.ac.ir/article_3130_5f9da27784144f3bc7107b0714462454.pdf
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10.22111/ijfs.2004.3130