ORIGINAL_ARTICLE
Cover Special Issue vol. 9, no. 6, December 2012
http://ijfs.usb.ac.ir/article_2806_bf0b8dbee5c9d7b98ae157ca9754c9e1.pdf
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10.22111/ijfs.2012.2806
ORIGINAL_ARTICLE
CREDIBILITY-BASED FUZZY PROGRAMMING MODELS TO
SOLVE THE BUDGET-CONSTRAINED FLEXIBLE
FLOW LINE PROBLEM
This paper addresses a new version of the exible ow line prob- lem, i.e., the budget constrained one, in order to determine the required num- ber of processors at each station along with the selection of the most eco- nomical process routes for products. Since a number of parameters, such as due dates, the amount of available budgets and the cost of opting particular routes, are imprecise (fuzzy) in practice, they are treated as fuzzy variables. Furthermore, to investigate the model behavior and to validate its attribute, we propose three fuzzy programming models based upon credibility measure, namely expected value model, chance-constrained programming model and dependent chance-constrained programming model, in order to transform the original mathematical model into a fuzzy environment. To solve these fuzzy models, a hybrid meta-heuristic algorithm is proposed in which a genetic al- gorithm is designed to compute the number of processors at each stage; and a particle swarm optimization (PSO) algorithm is applied to obtain the op- timal value of tardiness variables. Finally, computational results and some concluding remarks are provided.
http://ijfs.usb.ac.ir/article_110_4cdee35db4712858ef8408c9704bade8.pdf
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29
10.22111/ijfs.2012.110
Budget-constrained
exible
ow lines
Credibility-based fuzzy pro-
gramming
Meta-heuristic
Genetic algorithm
Particle swarm optimization
Ali
Ghodratnama
ghodratn@ut.ac.ir
true
1
Department of Industrial Engineering, College of Engineering,
University of Tehran, Tehran, Iran
Department of Industrial Engineering, College of Engineering,
University of Tehran, Tehran, Iran
Department of Industrial Engineering, College of Engineering,
University of Tehran, Tehran, Iran
AUTHOR
Seyed Ali
Torabi
satorabi@ut.ac.ir
true
2
Department of Industrial Engineering, College of Engineering,
University of Tehran, Tehran, Iran
Department of Industrial Engineering, College of Engineering,
University of Tehran, Tehran, Iran
Department of Industrial Engineering, College of Engineering,
University of Tehran, Tehran, Iran
LEAD_AUTHOR
Raza
Tavakkoli-Moghaddam
tavakoli@ut.ac.ir
true
3
Department of Industrial Engineering, College of En-
gineering, University of Tehran, Tehran, Iran
Department of Industrial Engineering, College of En-
gineering, University of Tehran, Tehran, Iran
Department of Industrial Engineering, College of En-
gineering, University of Tehran, Tehran, Iran
AUTHOR
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Journal of Fuzzy Systems, In press.
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94
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99
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101
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104
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110
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111
ORIGINAL_ARTICLE
AN ALGEBRAIC STRUCTURE FOR INTUITIONISTIC
FUZZY LOGIC
In this paper we extend the notion of degrees of membership and non-membership of intuitionistic fuzzy sets to lattices and introduce a residuated lattice with appropriate operations to serve as semantics of intuitionistic fuzzy logic. It would be a step forward to find an algebraic counterpart for intuitionistic fuzzy logic. We give the main properties of the operations defined and prove some theorems to demonstrate our goal.
http://ijfs.usb.ac.ir/article_111_92936ce87ae15b80c0c9d17ae0d847e8.pdf
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10.22111/ijfs.2012.111
Intuitionistic fuzzy logic
residuated lattice
Intuitionistic fuzzy
implication
Esfandiar
Eslami
esfandiar.eslami@uk.ac.ir
true
1
Department of Mathematics, Faculty of Mathematics and Com-
puter, Shahid Bahonar University of Kerman, Kerman, Iran
Department of Mathematics, Faculty of Mathematics and Com-
puter, Shahid Bahonar University of Kerman, Kerman, Iran
Department of Mathematics, Faculty of Mathematics and Com-
puter, Shahid Bahonar University of Kerman, Kerman, Iran
LEAD_AUTHOR
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1
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bibitem{18}A. Tepavcevic and M. G. Ranitovic, {it General form of lattice valued intuitionistic fuzzy sets}, Computational Intelligence, Theory and Applications, {bf14} (2006), 375-381.
18
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19
bibitem{20} E. Turunen, {it Mathematics behind fuzzy logic}, Advances in Soft Computing, Physica-Verlag, Heidelberg, 1999.
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bibitem{21} I. K. Vlachos and G. D. Sergiadis, {it Towards Intuitionistic fuzzy image processing}, Proceedings of the 2005 International Conference on Computational Intelligence for Modelling, Control and Automation.
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25
26
bibitem{24} L. A. Zadeh, {it Fuzzy sets, fuzzy logic and fuzzy systems}, Selected papers by Lotfi A. Zadeh, Editors George J. Klir and Bo Yuan, World Scientific, 1996.
27
ORIGINAL_ARTICLE
FUZZY GRADE OF THE COMPLETE HYPERGROUPS
This paper continues the study of the connection between hyper- groups and fuzzy sets, investigating the length of the sequence of join spaces associated with a hypergroup. The classes of complete hypergroups and of 1-hypergroups are considered and analyzed in this context. Finally, we give a method to construct a nite hypergroup with the strong fuzzy grade equal to a given natural number
http://ijfs.usb.ac.ir/article_112_0ff69d8d879db7c0ac75b78249b3499f.pdf
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10.22111/ijfs.2012.112
Complete hypergroup
Join space
Fuzzy set
Fuzzy grade
Carmen
Angheluta
floryangheluta@yahoo.com
true
1
Faculty of Mathematics and Computer Science, University of
Bucharest, Str. Academiei 14, 010014 Bucharest, Romania
Faculty of Mathematics and Computer Science, University of
Bucharest, Str. Academiei 14, 010014 Bucharest, Romania
Faculty of Mathematics and Computer Science, University of
Bucharest, Str. Academiei 14, 010014 Bucharest, Romania
AUTHOR
Irina
Cristea
irinacri@yahoo.co.uk
true
2
Center for Systems and Information Technologies, University of
Nova Gorica, Vipavska 13, SI-5000, Nova Gorica, Slovenia
Center for Systems and Information Technologies, University of
Nova Gorica, Vipavska 13, SI-5000, Nova Gorica, Slovenia
Center for Systems and Information Technologies, University of
Nova Gorica, Vipavska 13, SI-5000, Nova Gorica, Slovenia
AUTHOR
[1] R. Ameri and M. M. Zahedi,typergroup and join space induced by a fuzzy subset, PU.M.A.,8(1997).
1
[2] R. Ameri and O. R. Dehghan,Dimension of fuzzy hypervector spaces, Iranian Journal of Fuzzy Systems,8(5) (2011), 149-166.
2
[3] C. Angheluta and I. Cristea,On Atanassov's intuitionistic fuzzy grade of complete hyper-groups, J. Mult.-Valued Logic Soft Comput., 20 (2013), 55-74.
3
[4] P. Corsini,Prolegomena of Hypergroups Theory, Aviani Editore, Tricesimo, 1993.
4
[5] P. Corsini,Join spaces, power sets, fuzzy sets , Proc. Fifth International Congress on A.H.A.,1993, Iasi, Romania, Hadronic Press, (1994), 45-52.
5
[6] P. Corsini,A new connection between hypergroups and fuzzy sets, Southeast Asian Bull.Math.,27(2003), 221-229.
6
[7] P. Corsini and I. Cristea,Fuzzy grade of i.p.s. hypergroups of order 7, Iranian Journal of Fuzzy Systems,1(2) (2004), 15-32.
7
[8] P. Corsini and I. Cristea,Fuzzy sets and non complete 1-hypergroups, An. St. Univ. vidiusConstanta,13(1)(2005), 27-54.
8
[9] P. Corsini and V. Leoreanu,Join spaces associated with fuzzy sets, J. Combin. Inform. Syst.Sci.,20(1-4) 1995), 293-303.
9
[10] P. Corsini and V. Leoreanu,Applications of hyperstructure theory, Kluwer Academic Pub-lishers, Advances in Mathematics, 2003.
10
[11] P. Corsini and V. Leoreanu-Fotea,On the grade of a sequence of fuzzy sets and join spacesdetermined by a hypergraph, Southeast Asian Bull. Math., 34(2) (2010), 231-242.
11
[12] P. Corsini, V. Leoreanu-Fotea and A. Iranmanesh,On the sequence of hypergroups and mem-bership functions determined by a hypergraph, J. Mult.-Valued Logic Soft Comput., 14(6)(2008), 565-577.
12
[13] P. Corsini and R. Mahjoob,Multivalued functions, fuzzy subsets and join spaces, Ratio Math.,20(2010), 1-41.
13
[14] I. Cristea,Complete hypergroups, 1-Hypergroups and fuzzy sets, An. St. Univ. Ovidius Constanta,10(2) (2002), 25-38.
14
[15] I. Cristea,A property of the connection between fuzzy sets and hypergroupoids, Ital. J. Pure Appl. Math.,21 (2007), 73-82.
15
[16] I. Cristea,About the fuzzy grade of the direct product of two hypergroupoids, Iranian Journal of Fuzzy Systems,7(2) (2010), 95-108.
16
[17] I. Cristea and B. Davvaz,Atanassov's intuitionistic fuzzy grade of hypergroups, Information Sciences,180 (2010), 1506-1517.
17
[18] I. Cristea, M. Jafarpour and S. S. Mousavi,On fuzzy preordered structures and (fuzzy) hy-perstructures, Acta Math. Sin. (Engl. Ser.), 28(9) (2012), 1787-1798.
18
[19] B. Davvaz and M. Karimian,On the n-complete hypergroups, European J. Combin., 28(1)(2007), 86-93.[20] B. Davvaz and M. Karimian,On the n-complete hypergroups and K(H) hypergroups, ActaMath. Sin. (Engl. Ser.),24(11) (2008), 1901-1908.
19
[21] B. Davvaz, E. Hassani Sadrabadi and I. Cristea,Atanassov's intuitionistic fuzzy grade of the complete hypergroups of order less than or equal to6, submitted.
20
[22] B. Davvaz, E. Hassani Sadrabadi and I. Cristea,Atanassov's intuitionistic fuzzy grade of i.p.s. pergroups of order less than or equal to6, Iranian Journal of Fuzzy Systems,9(4)(2012), 71-97.
21
[23] B. Davvaz, E. Hassani Sadrabadi and I. Cristea,Atanassov's intuitionistic fuzzy grade of i.p.s.hypergroups of order7, J. Mult.-Valued Logic Soft Comput., accepted.
22
[24] H. Hedayati,Generalized fuzzy k-ideals of semirings with interval-valued membership func-tions, Bull.Malays.Math.Sci.Soc., 32(3) (2009), 409-424.
23
[25] J. Jantosciak,Homomorphism, equivalence and reductions in hypergroups, Riv. Mat. PuraAppl.,9(1991), 23-47.
24
[26] V. Leoreanu Fotea,t hypermodules, Comput. Math. Appl., 57(3) (2009), 466-47.
25
[27] V. Leoreanu Fotea and B. Davvaz,Fuzzy hyperrings, Fuzzy Sets and Systems, 160(16)(2009), 2366-2378.[28] F. Marty,Sur une generalization de la notion de groupe, Eight Congress Math. Scandenaves,Stockholm, (1934), 45-49.
26
[29] R. Migliorato,On the complete hypergroups, Riv. Mat. Pura Appl., 14 (1994), 21-31.
27
[30] W. Prenowits and J. Jantosciak,Geometries and join spaces, J. Reind und Angew Math.,257(1972), 100-128.
28
[31] A. Rosenfeld,Fuzzy groups, J. Math. Anal. Appl., 35 (1971), 512-517.
29
[32] M. Shabir, Y. B. Yun and M. Bano,On prime fuzzy bi-ideals of semigroups, Iranian Journal of Fuzzy Systems,7(3) (2010), 115-128.
30
[33] M. Stefanescu and I. Cristeon the fuzzy grade of the hypergroup, Fuzzy Sets and System159((2008), 1097-1106.
31
[34] Y. Yin, J. Zhan and X. Huang,A new way to fuzzy h-ideals of hemirings, Iranian Journal of Fuzzy Systems,8(5) (2011), 81-101.
32
[35] L. A. Zadeh,Fuzzy sets, Information and Control, 8 (1965), 338-353.
33
[36] J. Zhan and B. Davvaz,Study of fuzzy algebraic hypersystems from a general viewpoint, Int.J. Fuzzy Syst.,12(1) (2010), 73-79.
34
ORIGINAL_ARTICLE
DEFUZZIFICATION METHOD FOR RANKING FUZZY
NUMBERS BASED ON CENTER OF GRAVITY
Ranking fuzzy numbers plays a very important role in decision making and some other fuzzy application systems. Many different methods have been proposed to deal with ranking fuzzy numbers. Constructing ranking indexes based on the centroid of fuzzy numbers is an important case. But some weaknesses are found in these indexes. The purpose of this paper is to give a new ranking index to rank various fuzzy numbers effectively. Finally, several numerical examples following the procedure indicate the ranking results to be valid.
http://ijfs.usb.ac.ir/article_113_80aeb7a1e681279f864e912e3a6e965f.pdf
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10.22111/ijfs.2012.113
Ranking
Fuzzy numbers
Centroid point
Defuzzification
Tofigh
Allahviranloo
allahviranloo@yahoo.com
true
1
Department of Mathematics, Science and Research Branch,
Islamic Azad University, Tehran, Iran
Department of Mathematics, Science and Research Branch,
Islamic Azad University, Tehran, Iran
Department of Mathematics, Science and Research Branch,
Islamic Azad University, Tehran, Iran
LEAD_AUTHOR
Rahim
Saneifard
srsaneeifard@yahoo.com
true
2
Department of Mathematics, Science and Research Branch, Islamic
Azad University, Tehran, Iran
Department of Mathematics, Science and Research Branch, Islamic
Azad University, Tehran, Iran
Department of Mathematics, Science and Research Branch, Islamic
Azad University, Tehran, Iran
AUTHOR
[1] S. Abbasbandy and B. Asady,Ranking of fuzzy numbers by sign distance, Information Sciences,176(2006), 2405-2416.
1
[2] T. Allahviranloo and M. Afshar,Numerical methods for fuzzy linear partial differential equationsunder new ition for derivative, Iranian Journal of Fuzzy Systems, 7 (2010), 33-50.
2
[3] T. Allahviranloo, S. Abbasbandy and R. Saneifard,A method for ranking fuzzy numbers using new weighted distance , Mathematical and Computational Applications, 2 (2011), 359-369.
3
[4] T. Allahviranloo, S. Abbasbandy and R. Saneifard,An approximation approach for ranking fuzzy numbers based on weighted interval-value, Mathematical and Computational Applications,3(2011), 588-597.
4
[5] I. Altun,Some fixed point theorems for single and multi valued mappings on ordered nonarchimedean fuzzy metric spaces , Iranian Journal of Fuzzy Systems, 1 (2010), 91-96.
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[18] F. Merghadi and A. Alioughe,A related fixed point theorem in n fuzzy metric spaces, Iranian Journal of Fuzzy Systems,3 (2010), 73-86.
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[19] E. Pasha, A. Saiedfar and B. Asady,The percentiles of fuzzy numbers and their applications,Iranian Journal of Fuzzy Systems,6 (2009), 27-44.
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32
ORIGINAL_ARTICLE
A FUZZY DIFFERENCE BASED EDGE DETECTOR
In this paper, a new algorithm for edge detection based on fuzzyconcept is suggested. The proposed approach defines dynamic membershipfunctions for different groups of pixels in a 3 by 3 neighborhood of the centralpixel. Then, fuzzy distance and -cut theory are applied to detect the edgemap by following a simple heuristic thresholding rule to produce a thin edgeimage. A large number of experiments are employed to confirm the robustnessof the proposed algorithm. In the experiments different cases such as normalimages, images corrupted by Gaussian noise, and uneven lightening imagesare involved. The results obtained are compared with some famous algorithmssuch as Canny and Sobel operators, a competitive fuzzy edge detector, and astatistical based edge detector. The visual and quantitative comparisons showthe effectiveness of the proposed algorithm even for those images that werecorrupted by strong noise.
http://ijfs.usb.ac.ir/article_114_04f71f0b2f0662f7763880565a251dae.pdf
2012-12-02T11:23:20
2018-09-18T11:23:20
69
85
10.22111/ijfs.2012.114
Edge detection
Fuzzy edge detection
Dynamic membership function
Fuzzy dierence
Noisy images
M. A.
Nikouei Mahani
nikouei@mahani.info
true
1
Electrical Engineering Department, Shahid Bahonar Univer-
sity of Kerman, Kerman, Iran
Electrical Engineering Department, Shahid Bahonar Univer-
sity of Kerman, Kerman, Iran
Electrical Engineering Department, Shahid Bahonar Univer-
sity of Kerman, Kerman, Iran
AUTHOR
Mohamad
Koohi Moghadam
m koohi m@comp.iust.ac.ir
true
2
School of Computer Engineering, Iran University of
Science and Technology, Tehran, Iran
School of Computer Engineering, Iran University of
Science and Technology, Tehran, Iran
School of Computer Engineering, Iran University of
Science and Technology, Tehran, Iran
AUTHOR
Hosein
Nezamabadi-pour
nezam@mail.uk.ac.ir
true
3
Electrical Engineering Department, Shahid Bahonar Uni-
versity of Kerman, Kerman, Iran
Electrical Engineering Department, Shahid Bahonar Uni-
versity of Kerman, Kerman, Iran
Electrical Engineering Department, Shahid Bahonar Uni-
versity of Kerman, Kerman, Iran
AUTHOR
[1] O. AbuAarqob, N. Shawagfeh and O. AbuGhneim, Functions dened on fuzzy real numbers
1
according to zadehs extension, International Mathematical Forum, (2008), 763-776.
2
[2] A. J. Baddeley, An error metric for binary images, Robust Computer Vision-Quality of
3
Vision Algorithms, (1992), 59-78.
4
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5
Man, and CyberneticsPart C, 32(3) (2002), 252-260.
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7
IEEE Transaction on Fuzzy Systems, 6(1) (1998), 52-75.
8
[5] V. Boskovitz and H. Guterman, An adaptive neuro-fuzzy system for automatic image seg-
9
mentation and edge detection, IEEE Transactions on Fuzzy Systems, 10 (2002), 247-262.
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[6] D. Brzakovic and L. Hong, Road edge detection for mobile robot navigation, In Proceedings
11
of IEEE International Conference on Robotics and Automation, Scottsdale, AZ , USA, 2
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(1989), 1143-1147.
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[7] S. M. Chen, New methods for subjective mental workload assessment and fuzzy risk analysis,
14
Cybernetics and Systems, 27 (1996), 449-472.
15
[8] K. David A. and B. James C, Edge detection using a fuzzy neural network, Science of Articial
16
Neural Networks, 1710 (1992), 510-521.
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[9] C. Ducottet, T. Fournel and C. Barat, Scale-adaptive detection and local characterization of
18
edges based on wavelet transform, Signal Processing, 84(11) (2004), 2115-2137.
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[10] T. Hou and W. Kuo, A new edge detection method for automatic visual inspection, The
20
International Journal of Advanced Manufacturing Technology, 13 (1997), 407-412.
21
[11] J. S. R. Jang, C. T. Sun and E. Mizutani, Neuro-fuzzy and soft computing- a computational
22
approach to learning and machine intelligence, Prentice-Hall of India Pvt. Ltd., New Delhi,
23
[12] S. Karungaru, M. Fukumi, N. Akamatsu and T. Akashi, A simple 3D edge template for pose
24
invariant face detection, Lecture Notes in Computer Science, 4253 (2006), 692-698.
25
[13] H. Kim, J. Lee, D. Kim, H. Yoon and S. Chi, Motion and natural hand detection for gesture
26
recognition, SICE-ICASE,International Joint Conference, Busan,Korea, (2006), 313-316.
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[14] T. Law, H. Itoh and H. Seki, Image ltering, edge detection, and edge tracing using fuzzy
28
reasoning, IEEE Transactions on Pattern Analysis and Machine Intelligence, 18 (1996), 481-
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[15] L. R. Liang and C. G. Looney, Competitive fuzzy edge detection, Applied Soft Computing, 3
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(2003), 123-137.
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[16] D. H. Lim, Robust edge detection in noisy images, Computational Statistics and Data Anal-
32
ysis, 50 (2006), 803-812.
33
[17] C. Lopez-Molina, H. Bustince, J. Fernandez, P. Couto and B. De Baets, A gravitational
34
approach to edge detection based on triangular norms, Pattern Recognition, 43(11) (2010),
35
3730-3741.
36
[18] S. Lu, Z.Wang and J. Shen, Neuro-fuzzy synergism to the intelligent system for edge detection
37
and enhancement, Pattern Recognition, 36 (2003), 2395-2409.
38
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(1980), 187-217.
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[20] A. Maturo, On some structures of fuzzy numbers, Iranian Journal of Fuzzy Systems, 6 (2009),
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[21] R. Medina-Carnicer, A. Carmona-Poyato, R. Muoz-Salinas and F. J. Madrid-Cuevas, Deter-
42
mining hysteresis thresholds for edge detection by combiningthe advantages and disadvantages
43
of thresholding methods, IEEE Transactionson Image Processing, 19(1) (2010), 165-173.
44
[22] R. Medina-Carnicer, F. Madrid-Cuevas, A. Carmona-Poyato and R. M. noz Salinas, On
45
candidates selection for hysteresis thresholds in edge detection, PatternRecognition, 42(7)
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(2009), 1284-1296.
47
[23] R. Medina-Carnicer and F. Madrid-Cuevas, Unimodal thresholding for edgeDetection, Pattern
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Recognition, 41(7) (2008), 2337-2346.
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[24] R. Medina-Carnicer, F. Madrid-Cuevas, R. Muoz-Salinas and A. Carmona-Poyato, Solving
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the process of hysteresis without determining the optimal thresholds, Pattern Recognition,
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43(4) (2010), 1224-1232.
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[25] H. Meng, M. Freeman, N. Pears and C. Bailey, Real-time human action recognition on an em-
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bedded, recongurable video processing architecture, Journal of Real-Time Image Processing,
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3 (2008), 163-176.
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[26] S. Morillas, V. Gregori and Antonio. Hervs, Fuzzy peer groups for reducing mixed gaussian-
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impulse noise from color images, IEEE Transaction on Image Processing, 18(7) (2009),
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1452-1466.
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[27] J. Musevi-Niya and A. Aghagolzadeh, Adaptive directional wavelet-based edge detection, 2nd
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International Symposium on Telecommunications (IST2003), Isfahan, Iran, (2003), 191-195.
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[28] H. Nezamabadi-pour, S. Saryazdi and E. Rashedi, Edge detection using ant algorithms, Soft
61
Computing, 10 (2005), 623-628.
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63
Iranian Journal of Fuzzy Systems, 6 (2009), 27-44.
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[30] W. K. Pratt, Digital image processing, John Wiley and Sons, 2001.
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[35] B. Sridevi and R. Nadarajan, Fuzzy similarity measure for generalized fuzzy numbers, Inter-
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national Journal of Open Problems in Computer Science and Mathematics, 2 (2009), 240-253.
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Intelligence Laboratory, 1984.
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[38] D. Van De Ville, M. Nachtegael, D. Van Der Weken, E. Kerre, W. Philips and I. Lemahieu,
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Noise reduction by fuzzy image ltering, IEEE Transactions on Fuzzy Systems, 11 (2003),
81
[39] J. Wu, Z. Yin and Y. Xiong, The fast multilevel fuzzy edge detection of blurry images, IEEE
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Signal Processing Letters, 14 (2007), 344-347.
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[40] R. Xu and C. Li, Multidimensional least-squares tting with a fuzzy model, Fuzzy Sets and
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Systems, 119 (2001), 215-223.
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on HVS, Lecture Notes in Computer Science, 3801 (2005), 1051-1056.
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detection, Trans. Image Proc,18(15) (2009), 1057-7149.
89
[43] X. Zong and W. Liu, Fuzzy edge detection based on wavelets transform, Machine Learning
90
and Cybernetics, International Conference, (2008), 2869-2873.
91
ORIGINAL_ARTICLE
SYMMETRIC TRIANGULAR AND INTERVAL
APPROXIMATIONS OF FUZZY SOLUTION TO
LINEAR FREDHOLM FUZZY INTEGRAL
EQUATIONS OF THE SECOND KIND
In this paper a linear Fuzzy Fredholm Integral Equation(FFIE) with arbitrary Fuzzy Function input and symmetric triangular (Fuzzy Interval) output is considered. For each variable, output is the nearest triangular fuzzy number (fuzzy interval) to the exact fuzzy solution of (FFIE).
http://ijfs.usb.ac.ir/article_115_4867dc5fc41055bc015b22cd768c5f10.pdf
2012-12-02T11:23:20
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87
99
10.22111/ijfs.2012.115
Fuzzy number
Expected interval
Fuzzy integral equations
Symmetric
fuzzy number
Nystrom method
Majid
Alavi
M-alavi@Iau-arak.ac.ir
true
1
Department of Mathematics, Islamic Azad University, Arak Branch,
Arak, Iran
Department of Mathematics, Islamic Azad University, Arak Branch,
Arak, Iran
Department of Mathematics, Islamic Azad University, Arak Branch,
Arak, Iran
AUTHOR
Babak
Asady
B-asay@Iau-arak.ac.ir
true
2
Department of Mathematics, Islamic Azad University, Arak Branch,
Arak, Iran
Department of Mathematics, Islamic Azad University, Arak Branch,
Arak, Iran
Department of Mathematics, Islamic Azad University, Arak Branch,
Arak, Iran
AUTHOR
[1] S. Abbasbandy and T. Allahviranloo, Numerical solution of fuzzy dierential equation by
1
runge-Kutta method, Nonlinear Stud, 11 (2004), 117-129.
2
[2] S. Abbasbandy and B. Asady, The nearest trapezoidal fuzzy number to a fuzzy quantity, Appl
3
Math Comput, 156 (2004), 381-386.
4
[3] S. Abbasbandy, E. Babolian and M. Alavi, Numerical method for solving linear Fredholm
5
fuzzy integral equations of the second kind, Chaos, Soliton and Fractals, 31 (2007), 138-146.
6
[4] T. Allahviranloo and M. Otadi, Gaussian quadratures for approximate of fuzzy integrals,
7
Applied Mathematics and Computation, 170 (2005), 874-885.
8
[5] K. Atkinson, A survey of numerical methods for solving nonlinear integral equations, J. of
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Integral Equat. and Appl., 4 (1992), 15-46.
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[6] E. Babolian, H. Sadeghi Goghary and S. Abbasbandy, Numerical solution of linear Fredholm
11
fuzzy integral equations of the second kind by Adomian method, Applied Mathematics and
12
Computation, 161 (2005), 733-744
13
[7] E. Babolian, H. Sadeghi and S. Javadi, Numerically solution of fuzzy dierential equations
14
by Adomian method, Appl. Math. Comput., 149 (2004), 547-557.
15
[8] K. Balachandran and K. Kanagarajan, Existence of solutions of general nonlinear fuzzy
16
Voltera-Feredholm integral equations, J. Appl. Math. Stochastic Anal, 3 (2005), 333-343.
17
[9] K. Balachandran and P. Prakash, Existence of solution of nonlinear fuzzy Voltera-Feredholm
18
integral equations, Indian J. Pure Appl. Math, 333 (2002), 329-343.
19
[10] B. Bede and S. G. Gal, Quadrature rules for integral of fuzzy-number-valued functions, Fuzzy
20
Sets and Systems, 145 (2004), 359-380.
21
[11] A. M. Bica, Error estimation in the Approximation of the solution of nonlinear fuzzy Fered-
22
holm integral equations, Information Sciences, 174 (2008), 1279-1292.
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[12] J. J. Buckley and T. Furing, Fuzzy integral equations, J. Fuzzy Math, 10 (2002), 1011-1024.
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and Systems, 45 (1992), 189-202.
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[20] P. Diamond, Stability and periodicity in fuzzy dierential equations, IEEE Trans. Fuzzy Syst,
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8 (2000), 583-590.
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equations, Fuzzy Sets and Systems, 106 (1999), 35-48.
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[24] M. Fridman, M. Ming and A. Kandel, Solution to fuzzy integral equations with arbitrary
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kernels, Internat. J. Approx. Reason, 20 (1999), 249-262.
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[25] D. N. Georgion and I. E. Kougias, Bounded solutions for fuzzy integral equations, Int. j.
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(1987), 83-94
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terval -algorithms and properties, Fuzzy Sets and Systems, 159 (2008), 1354-1364.
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tions, Iranian Journal of Fuzzy Systems, 6 (2009), 27-44.
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Feredholm, integral equations, Fuzzy Sets and Systems, 115 (2000), 425-431.
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80
ORIGINAL_ARTICLE
THE RELATIONSHIP BETWEEN L-FUZZY PROXIMITIES AND
L-FUZZY QUASI-UNIFORMITIES
In this paper, we investigate the L-fuzzy proximities and the relationships betweenL-fuzzy topologies, L-fuzzy topogenous order and L-fuzzy uniformity. First, we show that the category of-fuzzy topological spaces can be embedded in the category of L-fuzzy quasi-proximity spaces as a coreective full subcategory. Second, we show that the category of L -fuzzy proximity spaces is isomorphic to the category of L-fuzzy topogenous order spaces. Finally,we obtain that the category of L-fuzzy proximity spaces can be embeddedin the category of L-fuzzy uniform spaces as a bireective full subcategory.
http://ijfs.usb.ac.ir/article_120_e9a1f2dafb8bbda298f95ab61533e4c2.pdf
2012-12-02T11:23:20
2018-09-18T11:23:20
101
111
10.22111/ijfs.2012.120
L-Fuzzy topology
L-fuzzy proximity
L-fuzzy uniformity
L-fuzzy
topogenous order
Fuzzy remote neighborhood systems
Eun-Seok
Kim
manmunje@hanmail.net
true
1
Department of Mathematics, Chonnam National University, 300 Yongbong-
dong, Bukgu, 500-757, Gwangju, Korea
Department of Mathematics, Chonnam National University, 300 Yongbong-
dong, Bukgu, 500-757, Gwangju, Korea
Department of Mathematics, Chonnam National University, 300 Yongbong-
dong, Bukgu, 500-757, Gwangju, Korea
AUTHOR
Seung-Ho
Ahn
shahn@chonnam.ac.kr
true
2
Department of Mathematics, Chonnam National University, 300 Yongbong-
dong, Bukgu, 500-757, GwangJu, Korea
Department of Mathematics, Chonnam National University, 300 Yongbong-
dong, Bukgu, 500-757, GwangJu, Korea
Department of Mathematics, Chonnam National University, 300 Yongbong-
dong, Bukgu, 500-757, GwangJu, Korea
AUTHOR
Dae Heui
Park
dhpark3331@chonnam.ac.kr
true
3
Department of Mathematics, Chonnam National University, 300 Yongbong-
dong, Bukgu, 500-757, GwangJu, Korea
Department of Mathematics, Chonnam National University, 300 Yongbong-
dong, Bukgu, 500-757, GwangJu, Korea
Department of Mathematics, Chonnam National University, 300 Yongbong-
dong, Bukgu, 500-757, GwangJu, Korea
AUTHOR
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Sons, New York, 1990.
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Anal. Appl., 9 (1984), 320-1337.
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[3] G. Artico and R. Moresco, Fuzzy proximities compatible with Lowen uniformities, Fuzzy Sets
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and Systems, 21 (1987), 85-99.
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[6] D. Dzajanbajev and A. Sostak, On a fuzzy uniform structure, Acta Comm. Univ. Tartu, 940
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Expositon, 26 (2006), 89-95.
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and E. P. Klement eds. Kluwer Acad. Publ., Dordrecht, Boston, London, Chapter 3, (2003),
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[16] W. J. Liu, Fuzzy proximity spaces redened, Fuzzy Sets and Systems, 15 (1985), 241-248.
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[20] F. G. Shi, The category of pointwise S-proximity spaces, Fuzzy Sets and Systems, 152 (2005),
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[22] A. Sostak, Basic structures of fuzzy topology, J. Math. Sciences, 78 (1996), 662-697.
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[23] A. Sostak, Fuzzy syntopogenous structures, Quaestiones Math., 20 (1997), 431-461.
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37
ORIGINAL_ARTICLE
Uniquely Remotal Sets in $c_0$-sums and $ell^infty$-sums of Fuzzy Normed Spaces
Let $(X, N)$ be a fuzzy normed space and $A$ be a fuzzy boundedsubset of $X$. We define fuzzy $ell^infty$-sums and fuzzy $c_0$-sums offuzzy normed spaces. Then we will show that in these spaces, all fuzzyuniquely remotal sets are singletons.
http://ijfs.usb.ac.ir/article_121_ff6e9553bb02f301be1760cab42930a1.pdf
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10.22111/ijfs.2012.121
Fuzzy normed spaces
Fuzzy remotal set
Alireza
Kamel Mirmostafaee
mirmostafaei@ferdowsi.um.ac.ir
true
1
Center of Excellence in Analysis on Algebraic Struc-
tures, Department of Pure Mathematics, Ferdowsi University of Mashhad, P. O. Box
1159, Mashhad 91775, Mashhad, Iran
Center of Excellence in Analysis on Algebraic Struc-
tures, Department of Pure Mathematics, Ferdowsi University of Mashhad, P. O. Box
1159, Mashhad 91775, Mashhad, Iran
Center of Excellence in Analysis on Algebraic Struc-
tures, Department of Pure Mathematics, Ferdowsi University of Mashhad, P. O. Box
1159, Mashhad 91775, Mashhad, Iran
AUTHOR
Madjid
Mirzavaziri
mirzavaziri@gmail.com
true
2
Center of Excellence in Analysis on Algebraic Structures, De-
partment of Pure Mathematics, Ferdowsi University of Mashhad, P. O. Box 1159, Mash-
had 91775, Mashhad, Iran
Center of Excellence in Analysis on Algebraic Structures, De-
partment of Pure Mathematics, Ferdowsi University of Mashhad, P. O. Box 1159, Mash-
had 91775, Mashhad, Iran
Center of Excellence in Analysis on Algebraic Structures, De-
partment of Pure Mathematics, Ferdowsi University of Mashhad, P. O. Box 1159, Mash-
had 91775, Mashhad, Iran
AUTHOR
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43
ORIGINAL_ARTICLE
(IC)LM-FUZZY TOPOLOGICAL SPACES
The aim of the present paper is to define and study (IC)$LM$-fuzzytopological spaces, a generalization of (weakly) induced $LM$-fuzzytopological spaces. We discuss the basic properties of(IC)$LM$-fuzzy topological spaces, and introduce the notions ofinterior (IC)-fication and exterior (IC)-fication of $LM$-fuzzytopologies and prove that {bf ICLM-FTop} (the category of(IC)$LM$-fuzzy topological spaces) is an isomorphism-closed fullproper subcategory of {bf LM-FTop} (the category of $LM$-fuzzytopological spaces) and {bf ICLM-FTop} is a simultaneouslybireflective and bicoreflective full subcategory of {bf LM-FTop}.
http://ijfs.usb.ac.ir/article_124_a929db0f489a56ad82899d80d76f5f06.pdf
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10.22111/ijfs.2012.124
LM-fuzzy topology
(IC) LM-fuzzy topological spaces
(IC)-fication
of LM-fuzzy topology
Category
Hai-Yang
Li
fplihiayang@126.com
true
1
School of Science, Xi'an Polytechnic University, Xi'an 710048, P. R.
China
School of Science, Xi'an Polytechnic University, Xi'an 710048, P. R.
China
School of Science, Xi'an Polytechnic University, Xi'an 710048, P. R.
China
AUTHOR
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ORIGINAL_ARTICLE
Persian-translation Vol.9, No.6
http://ijfs.usb.ac.ir/article_2807_82664d9575be6d31f502231085f96bf9.pdf
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10.22111/ijfs.2012.2807