ORIGINAL_ARTICLE
Cover vol. 14, no. 6, December 2017
http://ijfs.usb.ac.ir/article_3504_e75c3dcf8c1fe24b35436246ee473aff.pdf
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10.22111/ijfs.2017.3504
ORIGINAL_ARTICLE
F-TRANSFORM FOR NUMERICAL SOLUTION OF TWO-POINT BOUNDARY VALUE PROBLEM
We propose a fuzzy-based approach aiming at finding numerical solutions to some classical problems. We use the technique of F-transform to solve a second-order ordinary differential equation with boundary conditions. We reduce the problem to a system of linear equations and make experiments that demonstrate applicability of the proposed method. We estimate the order of accuracy of the proposed method. We show that the F-transform-based approach does not only extend the set of its applications, but has a certain advantage in the solution of ill-posed problems.
http://ijfs.usb.ac.ir/article_3495_ccab4ef5ff4c62aba8a2fe33db1a5b8e.pdf
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13
10.22111/ijfs.2017.3495
F-transform
Differential equation
Boundary value problem
Second order differential equation
Irina
Perfilieva
irina.perfilieva@osu.cz
true
1
University of Ostrava, Centre of Excellence IT4Innovations, Institute for Research and Applications of Fuzzy Modeling, 30. dubna 22, 701 03 Ostrava 1, Czech Republic
University of Ostrava, Centre of Excellence IT4Innovations, Institute for Research and Applications of Fuzzy Modeling, 30. dubna 22, 701 03 Ostrava 1, Czech Republic
University of Ostrava, Centre of Excellence IT4Innovations, Institute for Research and Applications of Fuzzy Modeling, 30. dubna 22, 701 03 Ostrava 1, Czech Republic
LEAD_AUTHOR
Petra
Stevuliakova
true
2
University of Ostrava, Centre of Excellence IT4Innovations,
Institute for Research and Applications of Fuzzy Modeling, 30. dubna 22, 701 03
Ostrava 1, Czech Republic
University of Ostrava, Centre of Excellence IT4Innovations,
Institute for Research and Applications of Fuzzy Modeling, 30. dubna 22, 701 03
Ostrava 1, Czech Republic
University of Ostrava, Centre of Excellence IT4Innovations,
Institute for Research and Applications of Fuzzy Modeling, 30. dubna 22, 701 03
Ostrava 1, Czech Republic
AUTHOR
Radek
Valasek
true
3
University of Ostrava, Centre of Excellence IT4Innovations, Institute for Research and Applications of Fuzzy Modeling, 30. dubna 22, 701 03 Ostrava 1,
Czech Republic
University of Ostrava, Centre of Excellence IT4Innovations, Institute for Research and Applications of Fuzzy Modeling, 30. dubna 22, 701 03 Ostrava 1,
Czech Republic
University of Ostrava, Centre of Excellence IT4Innovations, Institute for Research and Applications of Fuzzy Modeling, 30. dubna 22, 701 03 Ostrava 1,
Czech Republic
AUTHOR
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Berlin Heidelberg, 1992. ISBN 9783540548133.
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[2] U. Ascher, Collocation for two-point boundary value problems revisited, SIAM J. Numer.
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Anal. 23 (1986), 596{609.
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[4] N. S. Bakhvalov, N. P. Zhidkov and G. M. Kobelkov, Numerical Methods, Nauka, Moskva,
6
(1987), 600 p.
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[5] W. Chen and Y. Schen, Approximate solution for a class of second-order ordinary differenci-
8
tal equations by the fuzzy transform, Intelligent and Fuzzy Systems. 27 (2014), 73{82.
9
[6] H. Keller, Numerical Methods for Two-Point Boundary Value Problems, Ginn (Blaisdell),
10
Boston, 1968.
11
[7] A. Khastan, I. Perfilieva and Z. Alijani, A new fuzzy approximation method to Cauchy prob-
12
lems by fuzzy transform, Fuzzy Sets and Systems, 288 (2016), 75{95.
13
[8] A. Khastan, Z. Alijani and I. Perfilieva, Fuzzy transform to approximate solution of two-
14
point boundary value problems, Mathematical Methods in the Applied Sciences. (2016),
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http://dx.doi.org/10.1002/mma.3832.
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[9] I. Perfilieva, Fuzzy transforms: Theory and applications, Fuzzy Sets and Systems, 157 (2006),
17
[10] I. Perfilieva, Fuzzy approach to solution of differential equations with imprecise data: appli-
18
cation to reef growth problem, in: R.V. Demicco, G.J. Klir (Eds.), Fuzzy Logic in Geology,
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Academic Press, Amsterdam, (2003), 275-300.
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[11] I. Perfilieva, M. Holcapek and V. Kreinovich, A new reconstruction from the F-transform
21
components, Fuzzy Sets and Systems, 288 (2016), 3{25.
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[12] A. Quarteroni, R. Sacco and F. Saleri, Numerical Mathematics, Springer, New York, 2007.
23
[13] P. Stevuliakova, I. Perfilieva and R. Valasek, R., A New Approach to Fuzzy Boundary Value
24
Problem, in: World Scientific Proceedings Series on Computer Engineering and Information
25
Science, 10 (2016), 276{281.
26
ORIGINAL_ARTICLE
DIAGNOSIS OF BREAST LESIONS USING THE LOCAL CHAN-VESE MODEL, HIERARCHICAL FUZZY PARTITIONING AND FUZZY DECISION TREE INDUCTION
Breast cancer is one of the leading causes of death among women. Mammography remains today the best technology to detect breast cancer, early and efficiently, to distinguish between benign and malignant diseases. Several techniques in image processing and analysis have been developed to address this problem. In this paper, we propose a new solution to the problem of computer aided detection and interpretation for breast cancer. In the proposed approach, a Local Chan-Vese (LCV) model is used for the mass lesion segmentation step to isolate a suspected abnormality in a mammogram. In the classification step, we propose a two-step process: firstly, we use the hierarchical fuzzy partitioning (HFP) to construct fuzzy partitions from data, instead of using the only human information, available from expert knowledge, which are not sufficiently accurate and confronted to errors or inconsistencies. Secondly,fuzzy decision tree induction are proposed to extract classification knowledge from a set of feature-based examples. Fuzzy decision trees are first used to determine the class of the abnormality detected (well-defined mass, ill-defined mass, architectural distortion, or speculated masses), then, to identify the Severity of the abnormality, which can be benign or malignant. The proposed system is tested by using the images from Mammographic Image Analysis Society[MIAS] database. Experimental results show the efficiency of the proposed approach, resulting in an accuracy rate of 87, a sensitivity of 82.14\%, and good specificity of 91.42
http://ijfs.usb.ac.ir/article_3496_9e3953081f1d3b480e734c66534326f0.pdf
2017-12-30T11:23:20
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10.22111/ijfs.2017.3496
Breast cancer
Mass segmentation
Local Chan-Vese model fuzzy decision tree
Fuzzy partitioning
Computer-aided detection
Fouzia
Boutaouche
boutaouche-f@netcourrier.com
true
1
laboraoire SIMPA, Departement d'informatique, Faculte des
mathematiques et d'informatique, Universite des sciences et de la technologie d'Oran "Mohamed BOUDIAF", USTO-MB; BP 1505 El M'naouer 31000, Oran, Algerie
laboraoire SIMPA, Departement d'informatique, Faculte des
mathematiques et d'informatique, Universite des sciences et de la technologie d'Oran "Mohamed BOUDIAF", USTO-MB; BP 1505 El M'naouer 31000, Oran, Algerie
laboraoire SIMPA, Departement d'informatique, Faculte des
mathematiques et d'informatique, Universite des sciences et de la technologie d'Oran "Mohamed BOUDIAF", USTO-MB; BP 1505 El M'naouer 31000, Oran, Algerie
LEAD_AUTHOR
Nacéra
Benamrane
true
2
laboratoire SIMPA, Departement d'informatique, Faculte des
mathematiques et d'informatique, Universite des sciences et de la technologie d'Oran "Mohamed BOUDIAF", USTO-MB; BP 1505 El M'naouer 31000, Oran, Algerie
laboratoire SIMPA, Departement d'informatique, Faculte des
mathematiques et d'informatique, Universite des sciences et de la technologie d'Oran "Mohamed BOUDIAF", USTO-MB; BP 1505 El M'naouer 31000, Oran, Algerie
laboratoire SIMPA, Departement d'informatique, Faculte des
mathematiques et d'informatique, Universite des sciences et de la technologie d'Oran "Mohamed BOUDIAF", USTO-MB; BP 1505 El M'naouer 31000, Oran, Algerie
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areas on initial contour placement using the chan-vese method on digital mammograms,
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Computational and Mathematical Methods in Medicine, 2015 (2015), 1-16.
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[2] American Cancer Society, Cancer facts and figures, Atlanta, Ga: American Cancer Society,
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(2013), 1-60.
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[3] R. Bellotti, A completely automated CAD system for mass detection in a large mammographic
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database, Medical Physics, 33(8) (2006), 3066–3075.
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Review, 12(1) (1997), 1-40.
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using independent component analysis and neural networks, X Iberoamerican, Conference
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on Pattern Recognition, Havana, Lecture notes in computer science, 3773 (2005),
12
460–469.
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(2001), 266-277.
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[7] R. Crandall, Image segmentation using Chan Vese algorithm, ECE532 Project fall, 2009.
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Expert Syst Appl., 38(5)(2011), 5719–5726.
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[9] U. Khan, H. Shin, J. P. Choi and M. Kim, Weighted fuzzy decision trees for prognosis of
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breast cancer survivability, Proc of the Australasian Data Mining Conferenre, Glenelg, South
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Australia, 7(3) (2008), 141-152.
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[10] Z. Lei and K. Ardrew Chan, An artificial intelligent algorithm for tumor detection in screening
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mammogram, IEEE Trans. on Medical Imaging, 20(7) (2009), 559-567.
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[11] M. Leonardo de Oliveira, G. Braz Junior, C. S. .Aristofanes, A. Cardoso de Paiva and M.
24
Gattass, Detection of masses in digital mammograms using K-means and support vector
25
machine, Electronic Letters on Computer Vision and Image Analysis, 8(2) (2009), 39-50.
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[12] A. M.Maciej. Y.J.Lo, P.B.Harrawood and D. G Tourassc, Mutual information-based template
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matching scheme for detection of breast masses: From mammography to digital breast
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tissues using co-occurrence matrix and bayesian neural network in mammographic
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ORIGINAL_ARTICLE
INFORMATION MEASURES BASED TOPSIS METHOD FOR MULTICRITERIA DECISION MAKING PROBLEM IN INTUITIONISTIC FUZZY ENVIRONMENT
In the fuzzy set theory, information measures play a paramount role in several areas such as decision making, pattern recognition etc. In this paper, similarity measure based on cosine function and entropy measures based on logarithmic function for IFSs are proposed. Comparisons of proposed similarity and entropy measures with the existing ones are listed. Numerical results limpidly betoken the efficiency of these measures over others. An intuitionistic fuzzy weighted measures (IFWM) with TOPSIS method for multi-criteria decision making (MCDM) quandary is introduced to grade the alternatives. This approach is predicated on entropy and weighted similarity measures for IFSs. An authentic case study is discussed to rank the four organizations. To compare the different rankings, a portfolio selection problem is considered. Various portfolios have been constructed and analysed for their risk and return. It has been examined that if the portfolios are developed using the ranking obtained with proposed method, the return is increased with slight increment in risk.
http://ijfs.usb.ac.ir/article_3497_b79c7d8a35da236ddbc695172d3305b4.pdf
2017-12-30T11:23:20
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63
10.22111/ijfs.2017.3497
Fuzzy set
Intuitionistic fuzzy set
Entropy
Similarity measure
TOPSIS
MCDM
Arunodaya Raj
Mishra
true
1
Department of Mathematics, ITM University, Gwalior-
474001, M. P., India
Department of Mathematics, ITM University, Gwalior-
474001, M. P., India
Department of Mathematics, ITM University, Gwalior-
474001, M. P., India
LEAD_AUTHOR
Pratibha
Rani
pratibha138@gmail.com
true
2
Department of Mathematics, Jaypee University of Engineering and
Technology, Guna-473226, M. P., India
Department of Mathematics, Jaypee University of Engineering and
Technology, Guna-473226, M. P., India
Department of Mathematics, Jaypee University of Engineering and
Technology, Guna-473226, M. P., India
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Fuzzy Sets and Systems, 114 (2000), 01-09.
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set theory, Inform. and Control, 20 (1972), 301-312.
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[5] S. Ebrahimnejad, H. Hashemi, S. M. Mousavi and B. Vahdani, A new interval-valued intu-
8
itionistic fuzzy model to group decision making for the selection of outsourcing providers,
9
Journal of Economic Computation and Economic Cybernetics Studies and Research, 49
10
(2015), 269-290.
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[6] P. Grzegorzewski, On possible and necessary inclusion of intuitionistic fuzzy sets, Information
12
Sciences, 181 (2011), 342-350.
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[7] D. S. Hooda and A. R. Mishra, On trigonometric fuzzy information measures, ARPN Journal
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of Science and Technology, 05 (2015), 145-152.
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national Journal of Intelligent Systems, 23 (2008), 364-383.
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[12] W. L. Hung and M. S. Yang, Similarity measures of intuitionistic fuzzy sets based on Haus-
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mation measures and their applications in optimization principles, International Journal Of
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122
ORIGINAL_ARTICLE
MULTIPERIOD CREDIBILITIC MEAN SEMI-ABSOLUTE DEVIATION PORTFOLIO SELECTION
In this paper, we discuss a multiperiod portfolio selection problem with fuzzy returns. We present a new credibilitic multiperiod mean semi- absolute deviation portfolio selection with some real factors including transaction costs, borrowing constraints, entropy constraints, threshold constraints and risk control. In the proposed model, we quantify the investment return and risk associated with the return rate on a risky asset by its credibilitic expected value and semi- absolute deviation. Since the proposed model is a nonlinear dynamic optimization problem with path dependence, we design a novel forward dynamic programming method to solve it. Finally, we provide a numerical example to demonstrate the performance of the designed algorithm and the application of the proposed model.
http://ijfs.usb.ac.ir/article_3498_174cd4e7a275172b16ebbf632749d7bd.pdf
2017-12-30T11:23:20
2018-02-26T11:23:20
65
86
10.22111/ijfs.2017.3498
Finance
Multiperiod portfolio selection
Mean semi-absolute deviation
Entropy constraints
The forward dynamic programming method
Peng
Zhang
zhangpeng300478@aliyun.com
true
1
School of Economics and Management, South China Normal University,
Guangzhou 510006, P. R. China
School of Economics and Management, South China Normal University,
Guangzhou 510006, P. R. China
School of Economics and Management, South China Normal University,
Guangzhou 510006, P. R. China
LEAD_AUTHOR
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lio choice problem for a quadratic utility function, Annals of Operations Research, 229(1)
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exaggerated? The in
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fluence of downside risk in bear markets, Journal of International Money
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44(10) (2008), 2463{2473.
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kets using the meanCvariance approach, European Journal of Operational Research, 179(1)
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on fuzzy decision theory, European Journal of Operational Research, 175(2) (2006), 879{893.
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[7] N. Gulpnar and B. Rustem, Worst-case robust decisions for multi-period meanCvariance
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portfolio optimization, European Journal of Operational Research, 183(3) (2007), 981{1000.
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[8] X. Huang, Mean-semivariance models for fuzzy portfolio selection, Journal of Computational
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bankrupt control, Applied Mathematics and Computation, 218(22) (2012), 11196{11208.
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mulation, Mathematical Finance, 10(3) (2000), 387{406.
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transaction lots, European Journal of Operational Research, 242 (3) (2015), 933{941 .
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and a stochastic cash
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ow, Insurance: Mathematics and Economics, 50(3) (2012), 371{384.
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lection problem with risk control, OR Spectrum, 36(1) (2014), 113{132.
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102
ORIGINAL_ARTICLE
ON CONVERGENCE THEOREMS FOR FUZZY HENSTOCK INTEGRALS
The main purpose of this paper is to establish different types of convergence theorems for fuzzy Henstock integrable functions, introduced by Wu and Gong \cite{wu:hiff}. In fact, we have proved fuzzy uniform convergence theorem, convergence theorem for fuzzy uniform Henstock integrable functions and fuzzy monotone convergence theorem. Finally, a necessary and sufficient condition under which the point-wise limit of a sequence of fuzzy Henstock integrable functions is fuzzy Henstock integrable has been established.
http://ijfs.usb.ac.ir/article_3499_6f4cc2d0c57b2e8308bf4c95c9724bbb.pdf
2017-12-30T11:23:20
2018-02-26T11:23:20
87
102
10.22111/ijfs.2017.3499
Fuzzy number
Fuzzy number function
Fuzzy Henstock integral
Fuzzy monotone sequence
B. M.
Uzzal Afsan
true
1
Department of Mathematics, Sripat Singh College, Jiaganj-742123, Murshidabad, West Bengal, India
Department of Mathematics, Sripat Singh College, Jiaganj-742123, Murshidabad, West Bengal, India
Department of Mathematics, Sripat Singh College, Jiaganj-742123, Murshidabad, West Bengal, India
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20(2) (1994-95), 119{124.
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[4] Z. Gong, On the problem of characterizing derivatives for the fuzzy-valued functions (II):
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almost everywhere differentiability and strong Henstock integral, Fuzzy Sets and Systems,
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145 (2004), 381{393.
8
[5] Z. Gong and Y. Shao, The controlled convergence theorems for the strong Henstock integrals
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of fuzzy-number-valued functions, Fuzzy Sets and Systems, 160 (2009), 1528{1546.
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Systems, 120 (2001), 523{532.
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24
Systems, 44 (1991), 33{38.
25
ORIGINAL_ARTICLE
CREDIBILITY THEORY ORIENTED PREFERENCE INDEX FOR RANKING FUZZY NUMBERS
This paper suggests a novel approach for ranking the most applicable fuzzy numbers, i.e. $LR$-fuzzy numbers. Applying the $\alpha$-optimistic values of a fuzzy number, a preference criterion is proposed for ranking fuzzy numbers using the Credibility index. The main properties of the proposed preference criterion are also studied. Moreover, the proposed method is applied for ranking fuzzy numbers using target-rank-based methods. Some numerical examples are used to illustrate the proposed ranking procedure. The proposed preference criterion is also examined in order to compare with some common methods and the feasibility and effectiveness of the proposed ranking method is cleared via some numerical comparisons.
http://ijfs.usb.ac.ir/article_3500_c8f1b5309e61cf8692e840b8009dfcf8.pdf
2017-12-30T11:23:20
2018-02-26T11:23:20
103
117
10.22111/ijfs.2017.3500
Credibility index
$alpha$-optimistic values
Robustness
Reciprocity
Fuzzy target
Gholamreza
Hesamian
ghesamian@math.iut.ac.ir
true
1
Department of Statistics, Payame Noor University,, Tehran
19395-3697, Iran
Department of Statistics, Payame Noor University,, Tehran
19395-3697, Iran
Department of Statistics, Payame Noor University,, Tehran
19395-3697, Iran
LEAD_AUTHOR
Farid
Bahrami
f.ahmadi@math.iut.ac.ir
true
2
Department of Mathematical Sciences,, Isfahan University of Technology,, Isfahan 84156-83111, Iran
Department of Mathematical Sciences,, Isfahan University of Technology,, Isfahan 84156-83111, Iran
Department of Mathematical Sciences,, Isfahan University of Technology,, Isfahan 84156-83111, Iran
AUTHOR
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fuzzy numbers, Applied Intelligence, 26 (2007), 1-11.
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and right dominance, Computers and Mathematics with Applications, 41 (2001), 1589-1602.
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fuzzy relations, Fuzzy Sets and Systems, 26 (1988), 49-62.
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gyration, Computers and Mathematics with Applications, 51 (2006), 1127-1136.
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fuzzy numbers, Expert Systems with Applications, 39 (2012), 690-695.
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ables, Soft Computing, 20 (2015), 1537-1548.
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numbers and applications to target-oriented decision making, IEEE Transactions on Fuzzy
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fuzzy numbers, Expert Systems with Applications, 38 (2011), 10906{10910.
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decision-making, IEEE T. Fuzzy Syst., 7 (1999), 677-685.
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[26] K. H. Lee, First Course on Fuzzy Theory and Applications, Springer-Verlag, Berlin, 2005.
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fuzzy events, Computers and Mathematics with Applications, 15(1988), 887-896.
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to MADM problems, Computers and Mathematics with Applications, 60 (2010), 1557-1570.
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[31] X. W. Liu and S. L. Han, Ranking fuzzy numbers with preference weighting function expec-
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tations, Computers and Mathematics with Applications, 49 (2005), 1731-1753.
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[32] M. Modarres and S. S. Nezhad, Ranking fuzzy numbers by preference ratio, Fuzzy Sets and
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Systems, 118 (2001), 429-436.
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[33] J. Peng, H. Liu and G. Shang, Ranking fuzzy variables in terms of credibility measure,
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Proceedings of the 3th international conference on Fuzzy Systems and Knowledge Discovery,
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Xi'an, China, (2006), 24-28.
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[34] S. Rezvani, Ranking generalized exponential trapezoidal fuzzy numbers based on variance,
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Applied Mathematics and Computation, 262 (2015), 191{198.
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[35] J. Saade and H. Schwarzlander, Ordering fuzzy sets over the real line: an approach based on
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decision making under uncertainty, Fuzzy Sets and Systems, 50 (1992), 237-246.
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[36] H. Sun and J. Wu, A new approach for ranking fuzzy numbers based on fuzzy simulation
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analysis method, Applied Mathematics and Computation, 174 (2006), 755-767.
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[37] L. Tran and L. Duckstein, Comparison of fuzzy numbers using a fuzzy distance measure,
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[38] E. Valvis, A new linear ordering of fuzzy numbers on subsets of F(R), Fuzzy Optimazation
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and Decision Making, 8 (2009), 141-163.
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[39] Y. M. Wang, Centroid defuzzication and the maximizing set and minimizing set ranking
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based on alpha level sets, Computers and Industrial Engineering, 57 (2009), 228-236.
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[40] X. Wang and E. E. Kerre, Reasonable properties for the ordering of fuzzy quantities (I),
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Fuzzy Sets and Systems, 118 (2001), 387-405.
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[42] Y. J. Wang and H. S. Lee, The revised method of ranking fuzzy numbers with an area between
82
the centroid and original points, Computers and Mathematics with Applications, 55 (2008),
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2033-2042.
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85
degree, Information Science, 179 (2009), 2070-2077.
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[44] Y.M. Wang and Y. Luo, Area ranking of fuzzy numbers based on positive and negative ideal
87
points, Computers and Mathematics with Applications, 58 (2009), 1769-1779.
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and Engineering, 2 (2010), 27-36.
90
[46] P. Xu, X. Su, J. Wu, X. Sun, Y. Zhang and Y. Deng, A note on ranking generalized fuzzy
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numbers, Expert Systems with Applications, 39 (2012), 6454-6457.
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93
ORIGINAL_ARTICLE
SOME PROBABILISTIC INEQUALITIES FOR FUZZY RANDOM VARIABLES
In this paper, the concepts of positive dependence and linearlypositive quadrant dependence are introduced for fuzzy random variables. Also,an inequality is obtained for partial sums of linearly positive quadrant depen-dent fuzzy random variables. Moreover, a weak law of large numbers is estab-lished for linearly positive quadrant dependent fuzzy random variables. Weextend some well known inequalities to independent fuzzy random variables.Furthermore, a weak law of large numbers for independent fuzzy random vari-ables is stated and proved.
http://ijfs.usb.ac.ir/article_3501_4354e3e5670f8d20ba3ab0bb1f95685b.pdf
2017-12-30T11:23:20
2018-02-26T11:23:20
119
134
10.22111/ijfs.2017.3501
Fuzzy random variable
Linearly Positive Quadrant Dependence
Independence
Law of Large Numbers
Hamed
Ahmadzade
ahmadzadeh.h.63@gmail.com
true
1
Department of Statistics, University of Sistan and Baluchestan,
Zahedan, Iran
Department of Statistics, University of Sistan and Baluchestan,
Zahedan, Iran
Department of Statistics, University of Sistan and Baluchestan,
Zahedan, Iran
AUTHOR
Mohammad
Amini
mamini48@yahoo.com
true
2
Department of Statistics, Faculty of Mathematical Sciences,
Ferdowsi University of Mashhad, Mashhad 91775, Iran
Department of Statistics, Faculty of Mathematical Sciences,
Ferdowsi University of Mashhad, Mashhad 91775, Iran
Department of Statistics, Faculty of Mathematical Sciences,
Ferdowsi University of Mashhad, Mashhad 91775, Iran
LEAD_AUTHOR
Seyed Mahmoud
Taheri
sm_taheri@yahoo.com
true
3
Faculty of Engineering Science, College of Engineering,
University of Tehran, Tehran, Iran
Faculty of Engineering Science, College of Engineering,
University of Tehran, Tehran, Iran
Faculty of Engineering Science, College of Engineering,
University of Tehran, Tehran, Iran
AUTHOR
Abolghasem
Bozorgnia
a.bozorgnia@khayyam.ac.ir
true
4
Department of Statistics, Khayyam University, Mashhad,
Iran
Department of Statistics, Khayyam University, Mashhad,
Iran
Department of Statistics, Khayyam University, Mashhad,
Iran
AUTHOR
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Mathematics and Computation, 217(13) (2011), 6092{6096.
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dent fuzzy random variables, Thai Journal of Mathematics, 12 (2014), 537{548.
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fuzzy martingales and their applications, Journal Uncertainty Analysis and Applications, 2
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72
ORIGINAL_ARTICLE
ROBUSTNESS OF THE TRIPLE IMPLICATION INFERENCE METHOD BASED ON THE WEIGHTED LOGIC METRIC
This paper focuses on the robustness problem of full implication triple implication inference method for fuzzy reasoning. First of all, based on strong regular implication, the weighted logic metric for measuring distance between two fuzzy sets is proposed. Besides, under this metric, some robustness results of the triple implication method are obtained, which demonstrates that the triple implication method possesses a good behavior of robustness.
http://ijfs.usb.ac.ir/article_3502_3af181b32a04b615e978e930b50b64b4.pdf
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10.22111/ijfs.2017.3502
Robustness
Triple implication method
Weighted logic metric
Weighted logic similarity degree
Fuzzy reasoning
Jun
Li
lijun@lut.cn
true
1
School of Science, Lanzhou University of Technology, Lanzhou 730050,
Gansu, China
School of Science, Lanzhou University of Technology, Lanzhou 730050,
Gansu, China
School of Science, Lanzhou University of Technology, Lanzhou 730050,
Gansu, China
LEAD_AUTHOR
Chao
Fu
fuchao45612@sina.com
true
2
School of Science, Lanzhou University of Technology, Lanzhou 730050,
Gansu, China
School of Science, Lanzhou University of Technology, Lanzhou 730050,
Gansu, China
School of Science, Lanzhou University of Technology, Lanzhou 730050,
Gansu, China
AUTHOR
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5
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6
on Fuzzy Systems, 14(6) (2006), 709-715.
7
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8
on normalized Minkowski distances, Fuzzy Sets and Systems, 189 (2012), 63-73.
9
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reasoning, International Journal of Intelligent Systems, 20(4) (2005), 393-413.
22
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Computers and Mathematics with Applications, 56(8) (2008), 2079-2087.
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ORIGINAL_ARTICLE
ON STRATIFIED LATTICE-VALUED CONVERGENCE SPACES
In this paper we provide a common framework for different stratified $LM$-convergence spaces introduced recently. To this end, we slightly alter the definition of a stratified $LMN$-convergence tower space. We briefly discuss the categorical properties and show that the category of these spaces is a Cartesian closed and extensional topological category. We also study the relationship of our category to the categories of stratified $L$-topological spaces and of enriched $LM$-fuzzy topological spaces.
http://ijfs.usb.ac.ir/article_3503_08f77d70db13a2b7b2554e34ebcef52f.pdf
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164
10.22111/ijfs.2017.3503
Lattice-valued convergence
$LM$-convergence space
Stratified $LMN$-convergence tower space
Stratified $LM$-filter
Stratified $L$-topological space
Enriched $LM$-fuzzy topological space
Gunther
Jager
g.jager@ru.ac.za, gunther.jaeger@fh-stralsund.de
true
1
School of Mechanical Engineering, University of Applied Sciences
Stralsund, D-18435 Stralsund, Germany
School of Mechanical Engineering, University of Applied Sciences
Stralsund, D-18435 Stralsund, Germany
School of Mechanical Engineering, University of Applied Sciences
Stralsund, D-18435 Stralsund, Germany
LEAD_AUTHOR
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1
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3
Fuzzy Sets and Systems, 157 (2006), 2706 { 2714.
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11
135 { 142.
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17
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21
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(2012), 47 { 62.
23
ORIGINAL_ARTICLE
Persian-translation Vol.14, No.6
http://ijfs.usb.ac.ir/article_3505_70f72100261fda6aa83bea4e659894cb.pdf
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10.22111/ijfs.2017.3505