ORIGINAL_ARTICLE
Cover for Volume.13, No.2
http://ijfs.usb.ac.ir/article_2629_01f14615681aca7b51d39c6611ee7c7b.pdf
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10.22111/ijfs.2016.2629
ORIGINAL_ARTICLE
Fuzzy multi-criteria decision making method based on fuzzy structured element with incomplete weight information
The fuzzy structured element (FSE) theory is a very useful toolfor dealing with fuzzy multi-criteria decision making (MCDM)problems by transforming the criterion value vectors of eachalternative into the corresponding criterion function vectors. Inthis paper, some concepts related to function vectors are firstdefined, such as the inner product of two function vectors, thecosine of the included angle between two function vectors and theprojection of a function vector on another. Then a method based onFSE is developed to solve fuzzy MCDM problems in which thecriterion values take the form of general bounded closed fuzzynumbers and the criterion weight information is incompletecertain. In this method, the projections of criterion functionvectors on the fuzzy ideal function point (FIFP) are used to rankall the alternatives and then select the most desirable one, andan optimization model is constructed to determine the weights ofcriteria according to the incomplete weight information. Finally,an example is given to illustrate the feasibility andeffectiveness of the developed method.
http://ijfs.usb.ac.ir/article_2356_5a40d370d70c87597df91b924bd8af47.pdf
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10.22111/ijfs.2016.2356
Multi-criteria decision making (MCDM)
Fuzzy structured element (FSE)
Inner product
Projection
Entropy
Xinfan
Wang
true
1
School of Science, Hunan University of Technology, Zhuzhou, Hunan,
412007, China
School of Science, Hunan University of Technology, Zhuzhou, Hunan,
412007, China
School of Science, Hunan University of Technology, Zhuzhou, Hunan,
412007, China
AUTHOR
Jianqiang
Wang
true
2
School of Business, Central South University, Changsha, Hunan,
410083, China
School of Business, Central South University, Changsha, Hunan,
410083, China
School of Business, Central South University, Changsha, Hunan,
410083, China
LEAD_AUTHOR
Xiaohong
Chen
cxh@csu.edu.cn
true
3
School of Business, Central South University, Changsha, Hunan,
410083, China
School of Business, Central South University, Changsha, Hunan,
410083, China
School of Business, Central South University, Changsha, Hunan,
410083, China
AUTHOR
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ORIGINAL_ARTICLE
A NEW APPROACH BASED ON OPTIMIZATION OF RATIO FOR SEASONAL FUZZY TIME SERIES
In recent years, many studies have been done on forecasting fuzzy time series. First-order fuzzy time series forecasting methods with first-order lagged variables and high-order fuzzy time series forecasting methods with consecutive lagged variables constitute the considerable part of these studies. However, these methods are not effective in forecasting fuzzy time series which contain seasonal structures. In this respect, it would be more appropriate to use methods that consider the seasonal relations in seasonal fuzzy time series forecasting. Although seasonal fuzzy time series forecasting methods exist in literature, these methods use equal interval lengths in partition of the universe of discourse. This situation incapacitates the performance of the method in forecasting time series including seasonality and trend. In this study, a new fuzzy time series forecasting method in which intervals constituting partition of the universe of discourse increase in time at a rate that obtained based on optimization was proposed. The proposed method was applied to two real time series and obtained results were compared with other methods and the superior performance of the proposed method was proved.
http://ijfs.usb.ac.ir/article_2357_6875b4f9aecc564feec951e7b3ddb660.pdf
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36
10.22111/ijfs.2016.2357
Seasonal fuzzy time series
Optimization
Forecasting
Feed forward neural networks
Ufuk
Yolcu
uyolcu@ankara.edu.tr
true
1
Department of Statistics, Faculty of Science, Ankara University, 06100
Ankara, Turkey
Department of Statistics, Faculty of Science, Ankara University, 06100
Ankara, Turkey
Department of Statistics, Faculty of Science, Ankara University, 06100
Ankara, Turkey
LEAD_AUTHOR
[1] C. H. Aladag, M. A. Basaran, E. Egrioglu, U. Yolcu and V. R. Uslu, Forecasting in high
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Simulation, 81 (2010), 875-882.
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[3] C. H. Aladag, E. Egrioglu, U. Yolcu and V. R. Uslu, A high order seasonal fuzzy time series
7
model and application to international tourism demand of Turkey, Journal of Intelligent and
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Fuzzy Systems, 26 (2014), 295-302.
9
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series with genetic algorithms, Applied Soft Computing, 22 (2014), 465-473.
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series method, Hacettepe Journal of Mathematics and Statistics, 41 (2012), 375-385.
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ORIGINAL_ARTICLE
A Hybrid Multi-attribute Group Decision Making Method Based on Grey Linguistic 2-tuple
Because of the complexity of decision-making environment, the uncertainty of fuzziness and the uncertainty of grey maybe coexist in the problems of multi-attribute group decision making. In this paper, we study the problems of multi-attribute group decision making with hybrid grey attribute data (the precise values, interval numbers and linguistic fuzzy variables coexist, and each attribute value has a certain grey degree), and present a new grey hybrid multi-attribute group decision making method based on grey linguistic 2-tuple. Concretely, the concept of grey linguistic 2-tuple is defined based on the traditional linguistic 2-tuple, and the transformation methods of transforming the precise values, interval numbers and linguistic fuzzy variables into the grey linguistic 2-tuples are presented respectively. Further, a new grey linguistic 2-tuple weighted averaging (emph{GLTWA}) operator is presented to aggregate multiple decision makers' individual decision information into comprehensive decision information, and then a ranking method based on grey 2-tuple correlation degree is presented to rank all alternatives and to select the winners. An application decision making example of supplier selection is also given to validate the method developed and to highlight the implementation, practicality and effectiveness of the presented method.
http://ijfs.usb.ac.ir/article_2358_921ff0008ea7f62892c5003e6baad75f.pdf
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59
10.22111/ijfs.2016.2358
Hybrid multi-attribute group decision making
Grey linguistic 2-tuple
GLTWA operator
Grey 2-tuple correlation degree
Congjun
Rao
cjrao@foxmail.com
true
1
School of Science, Wuhan University of Technology, Wuhan 430070,
P. R. China
School of Science, Wuhan University of Technology, Wuhan 430070,
P. R. China
School of Science, Wuhan University of Technology, Wuhan 430070,
P. R. China
AUTHOR
Junjun
Zheng
jjzhengwhu@foxmail.com
true
2
School of Economics and Management, Wuhan University, Wuhan
430072, P. R. China
School of Economics and Management, Wuhan University, Wuhan
430072, P. R. China
School of Economics and Management, Wuhan University, Wuhan
430072, P. R. China
LEAD_AUTHOR
Cheng
Wang
wangc80@163.com
true
3
School of Mathematics and Economics, Hubei University of Education,
Wuhan 430072, P. R. China
School of Mathematics and Economics, Hubei University of Education,
Wuhan 430072, P. R. China
School of Mathematics and Economics, Hubei University of Education,
Wuhan 430072, P. R. China
AUTHOR
Xinping
Xiao
true
4
School of Science, Wuhan University of Technology, Wuhan 430070,
P. R. China
School of Science, Wuhan University of Technology, Wuhan 430070,
P. R. China
School of Science, Wuhan University of Technology, Wuhan 430070,
P. R. China
AUTHOR
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ORIGINAL_ARTICLE
A FUZZY-BASED SPEED CONTROLLER FOR IMPROVEMENT OF INDUCTION MOTOR'S DRIVE PERFORMANCE
Induction motors (IMs) are widely used in many industrial applications due to their robustness, low cost, simplicity and relative good efficiency. One of the major considerations for IMs is their speed control. PI (proportional-integrator) controllers are usually used as speed controller. Adjusting the gain of PI controller is time-consuming which needs thorough considerations. Hence, fuzzy controllers are proposed to overcome such problems. In this paper, firstly drive of a three-phase induction motor is designed based on PI controller and then fuzzy logic controller is implemented. This paper presents a novel speed control technique based on fuzzy logic with two inputs and one output for drive of an IM. The inputs are speed error and derivation of speed error and the output is speed. Finally comparison is done between the PI and fuzzy controllers which shows superiority of the fuzzy controller over PI controller.
http://ijfs.usb.ac.ir/article_2359_d0c6ddbaed27b7b597713095ad41c16e.pdf
2016-04-30T11:23:20
2018-02-17T11:23:20
61
70
10.22111/ijfs.2016.2359
Induction Motor
Speed Control
PI controller
Fuzzy Logic Controller
H.
Asgharpour-Alamdari
true
1
Department of Electrical Engineering, Semnan University, Semnan, Iran
Department of Electrical Engineering, Semnan University, Semnan, Iran
Department of Electrical Engineering, Semnan University, Semnan, Iran
LEAD_AUTHOR
Y.
Alinejad-Beromi
true
2
Department of Electrical Engineering, Semnan University, Semnan, Iran
Department of Electrical Engineering, Semnan University, Semnan, Iran
Department of Electrical Engineering, Semnan University, Semnan, Iran
AUTHOR
H.
Yaghobi
true
3
Department of Electrical Engineering, Semnan University, Semnan, Iran
Department of Electrical Engineering, Semnan University, Semnan, Iran
Department of Electrical Engineering, Semnan University, Semnan, Iran
AUTHOR
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1
asynchronous AC motor control by Particle Swarm Optimization (PSO) and Emperor Algo-
2
rithm, In Computer Modeling and Simulation (EMS), 2011 Fifth UKSim European Sympo-
3
sium , IEEE, (2011), 251-256.
4
[2] A. Al-Odienat and A. Al-Lawama, The advantages of PID fuzzy controllers over the conven-
5
tional types, American Journal of Applied Sciences 5(6) (2008), 653-658.
6
[3] D. Asija, Speed control of induction motor using fuzzy-PI controller, 2nd International Con-
7
ference In Mechanical and Electronics Engineering (ICMEE), 2(460) (2010).
8
[4] F. Barrero, et al, Speed control of induction motors using a novel fuzzy sliding-mode structure,
9
Fuzzy Systems, IEEE Transactions on, 10(3) (2002), 375-383.
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11
motor drive, Industrial Electronics, IEEE Transactions, 48(6) (2001), 1293-1295.
12
[6] V. Chitra and R. S. Prabhakar, Induction motor speed control using fuzzy logic controller,
13
World Academy of Science, Engineering and Technology, (23) (2006),17-22.
14
[7] R. Dhobale and D. M. Chandwadkar, FPGA Implementation of Three-Phase Induction Mo-
15
tor Speed Control Using Fuzzy Logic and Logic Based PWM, International Conference on
16
Recent Trends in Engineering & Technology, (2012), 185-189.
17
[8] A. Goedtel, I. N. Silva and P. J. A. Serni, Load torque identication in induction motor using
18
neural networks technique, Electric Power Systems Research, 77(1) (2007), 35-45.
19
[9] H. E. Kalhoodashti and M. Hahbazian, Hybrid Speed Control of Induction Motor using PI
20
and Fuzzy Controller, International Journal of Computer Applications, 30(11) (2011), 44-50.
21
[10] P. Kumar, V. Agarwal and A. K. Singh, Design of fuzzy PI controller for CSI Fed induction
22
motor drive, International Journal of Electrical and Electronic System Research, 1(4)(2011),
23
[11] F. Lima, et al,, Peed neuro-fuzzy estimator applied to sensorless induction motor contro,
24
Latin America Transactions, IEEE (Revista IEEE America Latina), 10(5) (2012), 2065-2073.
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26
troller. A real time implementation, ISA transactions, 52(3) (2013), 406-417.
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into account, Intelligent Control and Automation, (2012), 229-235.
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tor, International Journal of Emerging Technology and Advanced Engineering, 5(2)( 2012),
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ORIGINAL_ARTICLE
Alternating Regular Tree Grammars in the Framework of Lattice-Valued Logic
In this paper, two different ways of introducing alternation for lattice-valued (referred to as {L}valued) regular tree grammars and {L}valued top-down tree automata are compared. One is the way which defines the alternating regular tree grammar, i.e., alternation is governed by the non-terminals of the grammar and the other is the way which combines state with alternation. The first way is taken over to prove a main theorem: the class of languages generated by an {L}valued alternating regular tree grammar {LAG}) is equal to the class of languages accepted by an {L}valued alternating top-down tree automaton {LAA}). The second way is taken over to define a new type of automaton by combining the {L}valued alternating top-down tree automaton with stack, called {L}-valued alternating stack tree automaton {LASA} and the generative power of it is compared to some well-known language classes, especially to {LAA} and to {LAG}Also, we have derived a characterization of the state alternating regular tree grammar {LSAG}) in terms of {LASA}.
http://ijfs.usb.ac.ir/article_2360_9a374f39abaa600423ce805945586dd1.pdf
2016-04-30T11:23:20
2018-02-17T11:23:20
71
94
10.22111/ijfs.2016.2360
Lattice-valued logic
Alternating top-down tree automaton
State alternating regular tree grammar
Alternating stack tree automaton
Maryam
Ghorani
maryamghorani@gmail.com
true
1
Faculty of Mathematical Sciences, Shahrood University of Technology, Shahrood, Iran
Faculty of Mathematical Sciences, Shahrood University of Technology, Shahrood, Iran
Faculty of Mathematical Sciences, Shahrood University of Technology, Shahrood, Iran
LEAD_AUTHOR
Mohammad Mehdi
Zahedi
zahedi_mm@kgut.ac.ir
true
2
Department of Mathematics, Graduate University of Advanced Technology, Kerman, Iran
Department of Mathematics, Graduate University of Advanced Technology, Kerman, Iran
Department of Mathematics, Graduate University of Advanced Technology, Kerman, Iran
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and Computation, 205 (2007), 817-869.
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reduction and minimization, Fuzzy Sets and Systems, 161 (2010), 1635-1656.
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complete residuated lattice-valued logic, Fuzzy Sets and Systems, 158 (2007), 1407-1422.
102
ORIGINAL_ARTICLE
Algebraic Properties of Intuitionistic Fuzzy Residuated Lattices
In this paper, we investigate more relations between the symmetric residuated lattices $L$ with their corresponding intuitionistic fuzzy residuated lattice $tilde{L}$. It is shown that some algebraic structures of $L$ such as Heyting algebra, Glivenko residuated lattice and strict residuated lattice are preserved for $tilde{L}$. Examples are given for those structures that do not remain the same. Also some special subsets of $tilde{L}$ such as regular elements $Rg(tilde{L})$, dense elements $D(tilde{L})$, infinitesimal elements $Inf(tilde{L})$, boolean elements $B(tilde{L})$ and $Rad_{BL}(tilde{L})$ are characterized. The relations between these and corresponding sets in $L$ will be investigated.
http://ijfs.usb.ac.ir/article_2361_a03406f1388de5728fafa2ef1c358332.pdf
2016-04-30T11:23:20
2018-02-17T11:23:20
95
109
10.22111/ijfs.2016.2361
Intuitionstic fuzzy residuated lattice
Heyting algebra
Relative Stone lattice
Glivenko residuated lattice
MV (MTL
SRL)-algebra
Farnaz
Ghanavizi Maroof
farnaz.ghanavizi@yahoo.com
true
1
Department of Mathematics, Faculty of Mathematics and
Compute, Shahid Bahonar University of Kerman, 76169-14111, Kerman, Iran
Department of Mathematics, Faculty of Mathematics and
Compute, Shahid Bahonar University of Kerman, 76169-14111, Kerman, Iran
Department of Mathematics, Faculty of Mathematics and
Compute, Shahid Bahonar University of Kerman, 76169-14111, Kerman, Iran
AUTHOR
Esfandiar
Eslami
esfandiar.eslami@uk.ac.ir
true
2
Department of Mathematics, Faculty of Mathematics and Com-
pute, Shahid Bahonar University of Kerman, 76169-14111, Kerman, Iran
Department of Mathematics, Faculty of Mathematics and Com-
pute, Shahid Bahonar University of Kerman, 76169-14111, Kerman, Iran
Department of Mathematics, Faculty of Mathematics and Com-
pute, Shahid Bahonar University of Kerman, 76169-14111, Kerman, Iran
LEAD_AUTHOR
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[2] K. T. Atanassov and S. Stoeva, Intuitionistic L-fuzzy sets, in:R.Trapple (ed.), Elsevier
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Science Publishers B.V., North Holland, 1984.
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Systems, 145 (1998), 267{277.
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on the properties of Intuitionistic Fuzzy relations, Mathware and Soft Computing, 2 (1995),
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extensions (A terminological debate on Atanassov IFS), Fuzzy Sets and Systems, 24 (2006),
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3198{3219.
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[6] R. Cignoli and F. Esteva, Commutative integral bounded residuated lattices with an added
11
involution, Annals of Pure and Applied Logic, 171 (2009), 150{170.
12
[7] C. Cornelis and G. Deschrijver and E. E. Kerre, Classication on intuitionistic fuzzy impli-
13
cators: an algebraic approach, In Proceedings of the FT & T' 02, Durham, North Carolina,
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[8] D. Dubois and S. Gottwald and P. Hajek and J. Kacprzyk and H. Prade, Terminological dif-
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culties in fuzzy set theory- The case of "Intuitionistic Fuzzy Sets", Fuzzy Sets and Systems,
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156 (2005), 485{491.
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[9] G. Deschrijver and C. Cornelis and E. E. Kerre, Intuitionistic fuzzy connectives revisited, In
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proceedings of IPMU'02, 2002.
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[10] E. Eslami, An algebraic structure for Intuitionistic Fuzzy Logic, Iranian Journal of Fuzzy
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Systems, 9(6) (2012), 31{41.
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[11] E. Eslami and W. Peng-Yung, More on intutionistic fuzzy residuated lattices, Journal of
22
Multiple-Valued Logic and Soft Computing, 20(3) (2013), 335{352.
23
[12] E. Eslami and F. Ghanavizi Maroof, A Proposed axiomatic system for atanassov intuition-
24
istic fuzzy logic (A-IFL), Notes on Intuitionistic Fuzzy Sets, 19(3) (2013), 1{14.
25
[13] P. Hajek, Metamathematics of fuzzy logic, Trends in Logic, Kluwer Academic Publishers,
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Drdrecht, 1998.
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Intuitionistic Fuzzy Bi- ideals, Mathware and Soft Computing, 14 (2007), 57{66.
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[15] M. Kondo, Note on strict residuated lattices with an involutive negation, AAA80 Workshop
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on General Algebra& Workshop on Non- classical algebraic Structures, Bedlewo, Poland, 1-6
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june, 2010.
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Roumanie Tome, 53(1) (2010), 11{24.
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[17] H. Ono, Substructural logics and residuated lattices - an introduction, Trends in Logic,
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(2003), 177{212.
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[19] E. Szmidt and K. Marta, Atanassov's intuitionistic fuzzy sets in classication of imbalanced
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and overlapping classes. intelligent techniques and tools for novel system architectures, Studies
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in Computational Intelligence (SCI), 109 (2008), 455{471.
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Computational Intelligence, Theory and Applications, Springer Berlin Heidelberg, Germany,
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(2006), 375{381.
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of Symbolic Logic, 49(3) (1984), 851{866.
47
ORIGINAL_ARTICLE
Width invariant approximation of fuzzy numbers
In this paper, we consider the width invariant trapezoidal and triangularapproximations of fuzzy numbers. The presented methods avoid the effortful computation of Karush-Kuhn-Tucker Theorem. Some properties of the new approximation methods are presented and the applicability of the methods is illustrated by examples. In addition, we show that the proposed approximations of fuzzy numbers preserve the expected value too.
http://ijfs.usb.ac.ir/article_2362_b6a4f056d6dc711eada410b16ef83211.pdf
2016-04-30T11:23:20
2018-02-17T11:23:20
111
130
10.22111/ijfs.2016.2362
Extended trapezoidal fuzzy numbers
Trapezoidal approximations
Triangular approximations
Width
Expected value
Alireza
Khastan
khastan@iasbs.ac.ir
true
1
Department of Mathematics, Institute for Advanced Studies in
Basic Sciences, Zanjan, Iran
Department of Mathematics, Institute for Advanced Studies in
Basic Sciences, Zanjan, Iran
Department of Mathematics, Institute for Advanced Studies in
Basic Sciences, Zanjan, Iran
LEAD_AUTHOR
Zahra
Moradi
zahramoradi@iasbs.ac.ir
true
2
Department of Mathematics, Institute for Advanced Studies in Basic
Sciences, Zanjan, Iran
Department of Mathematics, Institute for Advanced Studies in Basic
Sciences, Zanjan, Iran
Department of Mathematics, Institute for Advanced Studies in Basic
Sciences, Zanjan, Iran
AUTHOR
[1] S. Abbasbandy and M. Amirfakhrian, The nearest approximation of a fuzzy quantity in
1
parametric form, Applied Mathematics and Computation, 172 (2006), 624–632.
2
[2] S. Abbasbandy and M. Amirfakhrian, The nearest trapezoidal form of a generalized left right
3
fuzzy number, International Journal of Approximate Reasoning, 43 (2006), 166–178.
4
[3] S. Abbasbandy and B. Asady, The nearest trapezoidal fuzzy number to a fuzzy quantity,
5
Applied Mathematics and Computation, 156 (2004), 381–386.
6
[4] S. Abbasbandy and T. Hajjari, Weighted trapezoidal approximation-preserving core of a fuzzy
7
number, Computers and Mathematics with Applications, 59 (2010),3066–3077.
8
[5] T. Allahviranloo and M. Adabitabar Firozja, Note on "Trapezoidal approximation of fuzzy
9
numbers", Fuzzy Sets and Systems, 158 (2007), 755–756.
10
[6] A. I. Ban, Approximation of fuzzy numbers by trapezoidal fuzzy numbers preserving the
11
expected interval, Fuzzy Sets and Systems, 159 (2008), 1327-1344.
12
[7] A. I. Ban, Trapezoidal and triangular approximations of fuzzy numbers-inadvertences and
13
corrections, Fuzzy Sets and Systems, 160 (2009), 3048-3058.
14
[8] A. I. Ban, A. Brandas, L. Coroianu, C. Negrutiu and O. Nica, Approximations of fuzzy
15
numbers by trapezoidal fuzzy numbers preserving the ambiguity and value, Computers and
16
Mathematics with Applications, 61 (2011), 1379-1401.
17
[9] A. I. Ban and L. Coroianu, Translation invariance and scale invariance of approximations of
18
fuzzy numbers, in: 7th Conference of the European Society for Fuzzy Logic and Technology,
19
Aix-Les-Bains, 18-22 July 2011.
20
[10] A. I. Ban and L. Coroianu, Nearest interval, triangular and trapezoidal approximation of
21
a fuzzy number preserving ambiguity, International Journal of Approximate Reasoning, 53
22
(2012), 805–836.
23
[11] A.I. Ban, L. Coroianu, Existence, uniqueness and continuity of trapezoidal approximations
24
of fuzzy numbers under a general condition, Fuzzy Sets and Systems, 257(2014), 3-22.
25
[12] A. Brandas, Approximation of fuzzy numbers by trapezoidal fuzzy numbers preserving the
26
core, the ambiguity and the value, Advanced Studies in Contemporary Mathematics, 21
27
(2011), 247259.
28
[13] S. Bodjanova, Median value and median interval of a fuzzy number, Information Sciences,
29
172 (2005), 73-89.
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[14] S. Chanas, On the interval approximation of a fuzzy number, Fuzzy Sets and Systems, 122
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(2001), 353-356.
32
[15] L. Coroianu, M. Gagolewski and P. Grzegorzewski, Nearset piecewise linear approximation
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of fuzzy numbers, Fuzzy Sets and Systems, 233 (2013), 26-51.
34
[16] P. Diamond and P. Kloeden, Metric spaces of fuzzy sets, theory and applications, World
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Scientific, Singapore, 1994.
36
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38
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(1998), 83-94.
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[20] P. Grzegorzewski, Nearest interval approximation of a fuzzy number, Fuzzy Sets and Systems,
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130 (2002), 321-330.
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Systems, 153 (2005), 115-135.
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[22] P. Grzegorzewski, E. Mr´owka, Trapezoidal approximations of fuzzy numbers-revisited, Fuzzy
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Sets and Systems, 158 (2007), 757-768.
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[25] C. T. Yeh, A note on trapezoidal approximation of fuzzy numbers, Fuzzy Sets and Systems,
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158 (2007), 747-754.
50
[26] C. T. Yeh, On improving trapezoidal and triangular approximations of fuzzy numbers, International
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Journal of Approximate Reasoning, 48 (2008), 297-313.
52
[27] C. T. Yeh, Trapezoidal and triangular approximations preserving the expected interval, Fuzzy
53
Sets and Systems, 159 (2008), 1345–1353.
54
[28] C. T. Yeh, Weighted trapezoidal and triangular approximations of fuzzy numbers, Fuzzy Sets
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and Systems, 160 (2009), 3059–3079.
56
[29] C. T. Yeh, Weighted semi-trapezoidal approximations of fuzzy numbers, Fuzzy Sets and Systems,
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165 (2011), 61-80.
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257 (2014) 23-40.
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[31] W. Zeng, H. Li, Weighted triangular approximation of fuzzy numbers, International Journal
61
of Approximate Reasoning, 46 (2007), 137–150.
62
ORIGINAL_ARTICLE
Irreducibility on General Fuzzy Automata
The aim of this paper is the study of a covering of a max-mingeneral fuzzy automaton by another, admissible relations, admissiblepartitions of a max-min general fuzzy automaton,$tilde{delta}$-orthogonality of admissible partitions, irreduciblemax-min general fuzzy automata. Then we obtain the relationshipsbetween them.
http://ijfs.usb.ac.ir/article_2363_d9fe18c52e5072bf681626d5f67c6ab5.pdf
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131
144
10.22111/ijfs.2016.2363
(General) Fuzzy automata
Equivalence relation
Admissible relation
Admissible partition
Irreducibility
Mohammad
Horry
true
1
Shahid Chamran University of Kerman, Kerman, Iran
Shahid Chamran University of Kerman, Kerman, Iran
Shahid Chamran University of Kerman, Kerman, Iran
LEAD_AUTHOR
[1] M. Doostfatemeh and S. C. Kremer, New directions in fuzzy automata, International Journal
1
of Approximate Reasoning, 38 (2005), 175{214.
2
[2] M. Horry and M. M. Zahedi, Fuzzy subautomata of an invertible general fuzzy automaton,
3
Annals of fuzzy sets, fuzzy logic and fuzzy systems, 2(2) (2013), 29{47.
4
[3] J. Jin, Q. Li and Y. Li, Algebric properties of L-fuzzy nite automata, Information Sciences,
5
234 (2013), 182-202.
6
[4] Y. Li and W. Pedrycz, Fuzzy nite automata and fuzzy regular expressions with membership
7
values in lattice-ordered monoids, Fuzzy Sets and Systems, 156 (2005), 68{92.
8
[5] J. N. Mordeson and D. S. Malik, Fuzzy automata and languages, theory and applications,
9
Chapman and Hall/CRC, London/Boca Raton, FL, 2002.
10
[6] D. S. Malik, J. N. Mordeson and M. K. Sen, On subsystems of fuzzy nite state machines,
11
Fuzzy Sets and Systems, 68 (1994), 83{92.
12
[7] M. Mizumoto, J. Tanaka and K. Tanaka, Some consideration on fuzzy automata, J. Compute.
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Systems Sci., 3 (1969), 409{422.
14
[8] W. Omlin, K. K. Giles and K. K. Thornber, Equivalence in knowledge representation: au-
15
tomata, rnns, and dynamic fuzzy systems, Proc. IEEE, 87(9) (1999), 1623{1640.
16
[9] W. Omlin, K. K. Thornber and K. K. Giles, Fuzzy nite-state automata can be deterministi-
17
cally encoded into recurrent neural networks, IEEE Trans. Fuzzy Syst., 5(1) (1998), 76{89.
18
[10] E. S. Santos, Realization of fuzzy languages by probabilistic, max-prod and maximin au-
19
tomata, Inform. Sci., 8 (1975), 39{53.
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[11] S. P. Tiwari, A. K. Singh, S. Sharan and V. K. Yadav Bifuzzy core of fuzzy automata, Iranian
21
Journal of Fuzzy Systems, 12(2) (2015), 63{73.
22
[12] W. G. Wee, On generalization of adaptive algorithm and application of the fuzzy sets concept
23
to pattern classif ication, Ph.D. dissertation Purdue University, IN, 1967.
24
[13] M. M. Zahedi, M. Horry and Kh. Abolpor, Bifuzzy (General) topology on max-min general
25
fuzzy automata, Advanced in Fuzzy Mathematics, 3(1) (2008), 51{68.
26
ORIGINAL_ARTICLE
On metric spaces induced by fuzzy metric spaces
For a class of fuzzy metric spaces (in the sense of George and Veeramani) with an H-type t-norm, we present a method to construct a metric on a fuzzy metric space. The induced metric space shares many important properties with the given fuzzy metric space. Specifically, they generate the same topology, and have the same completeness. Our results can give the constructive proofs to some problems for fuzzy metric spaces in the literature, which are shown by examples in this paper.
http://ijfs.usb.ac.ir/article_2364_95750b4afd0e7c65b341f7f2c7bce7d7.pdf
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145
160
10.22111/ijfs.2016.2364
Fuzzy analysis
Complete metric spaces
Fuzzy metric
H-type t-norms
D.
Qiu
true
1
College of Mathematics and Physics,, Chongqing University of Posts and
Telecommunications,, Nanan, Chongqing, 400065, P. R. China
College of Mathematics and Physics,, Chongqing University of Posts and
Telecommunications,, Nanan, Chongqing, 400065, P. R. China
College of Mathematics and Physics,, Chongqing University of Posts and
Telecommunications,, Nanan, Chongqing, 400065, P. R. China
LEAD_AUTHOR
R.
Dong
true
2
College of Mathematics and Physics,, Chongqing University of Posts and
Telecommunications,, Nanan, Chongqing, 400065, P. R. China
College of Mathematics and Physics,, Chongqing University of Posts and
Telecommunications,, Nanan, Chongqing, 400065, P. R. China
College of Mathematics and Physics,, Chongqing University of Posts and
Telecommunications,, Nanan, Chongqing, 400065, P. R. China
AUTHOR
H.
Li
true
3
College of Mathematics and Physics,, Chongqing University of Posts and
Telecommunications,, Nanan, Chongqing, 400065, P. R. China
College of Mathematics and Physics,, Chongqing University of Posts and
Telecommunications,, Nanan, Chongqing, 400065, P. R. China
College of Mathematics and Physics,, Chongqing University of Posts and
Telecommunications,, Nanan, Chongqing, 400065, P. R. China
AUTHOR
[1] T. Altun and D. Mihet, Ordered non-archimedean fuzzy metric spaces and some xed point
1
results, Fixed Point Theory Appl., Article ID 782680, 2010, 11 pages.
2
[2] D. Burago, Y. Burago and S. Ivanov, A course in metric geometry, American Mathematical
3
Society, Ann Arbor, 2001.
4
[3] A. George and P. Veeramani, On some results in fuzzy metric spaces, Fuzzy Set Syst., 64(2)
5
(1994), 395{399.
6
[4] A. George and P. Veeramani, On some results of analysis for fuzzy metric spaces, Fuzzy Set
7
Syst., 90(2) (1997), 365{368.
8
[5] M. Grabiec, Fixed points in fuzzy metric spaces, Fuzzy Set Syst., 27(2) (1989), 385{389.
9
[6] V. Gregori, A. Lopez-Crevillen, S. Morillas and A. Sapena, On convergence in fuzzy metric
10
spaces, Topol. Appl., 156 (18) (2009), 3002{3006.
11
[7] V. Gregori, J. Mi~nana and S. Morillas, Some questions in fuzzy metric spaces, Fuzzy Set
12
Syst., 204(1) (2012), 71{85.
13
[8] V. Gregori, J. Mi~nana and S. Morillas, A note on local bases and convergence in fuzzy metric
14
spaces, Topol. Appl., 163 (15) (2014), 142{148.
15
[9] V. Gregori, S. Morillas and A. Sapena, On a class of completable fuzzy metric spaces, Fuzzy
16
Set Syst., 161(5) (2010), 2193{2205.
17
[10] V. Gregori, S. Morillas and A. Sapena, Examples of fuzzy metrics and applications, Fuzzy
18
Set Syst., 107(1) (2011), 95{111.
19
[11] V. Gregori and S. Romaguera, Some properties of fuzzy metric spaces, Fuzzy Set Syst.,
20
115(3) (2000), 485{489.
21
[12] V. Gregori and S. Romaguera, On completion of fuzzy metric spaces, Fuzzy Set Syst., 130
22
(3) (2002), 399{404.
23
[13] V. Gregori and S. Romaguera, Characterizing completable fuzzy metric spaces, Fuzzy Set
24
Syst., 144 (3) (2004), 411{420.
25
[14] V. Gregori and A. Sapena, On xed point theorems in fuzzy metric spaces, Fuzzy Set Syst.,
26
125 (2) (2002), 245{252.
27
[15] O. Hadzic and E. Pap, Fixed point theory in probabilistic metric spaces, Kluwer Academic
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Publishers, Dordrecht, 2001.
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[16] O. Kaleva and S. Seikkala, On fuzzy metric spaces, Fuzzy Set Syst., 12(1) (1984), 215{229.
30
[17] E. Klement, R. Mesiar and E. Pap, Triangular norms, Kluwer, Dordrecht (2000).
31
[18] I. Kramosil and J. Michalek, Fuzzy metric and statistical metric spaces, Kybernetika, 11(2)
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(1975), 326{334.
33
[19] C. Li, On some results of metrics induced by a fuzzy ultrametric, Filomat, 27(6) (2013),
34
1133{1140.
35
[20] K. Menger, Statistical metrics, Proc. Natl. Acad. Sci. USA, 28(1) (1942), 535{537.
36
[21] D. Mihet, Fuzzy -contractive mappings in non-Archimedean fuzzy metric spaces, Fuzzy Set
37
Syst., 159(4) (2008), 739{744.
38
[22] E. Pap, O. Hadzio and R. Mesiar, A xed point theorem in probabilistic metric spaces and
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applications in fuzzy set theory, J. Math. Anal. Appl., 202 (2) (1996), 433{449.
40
[23] D. Qiu and W. Zhang, The strongest t-norm for fuzzy metric spaces, Kybernetika,49 (1)
41
(2013), 141{148.
42
[24] D. Qiu, W. Zhang and C. Li, Extension of a class of decomposable measures using fuzzy
43
pseudometrics, Fuzzy Set Syst., 222(1) (2013), 33{44.
44
[25] V. Radu, Some suitable metrics on fuzzy metric spaces, Fixed Point Theory, 5 (2) (2004),
45
[26] A. Razani, A contraction theorem in fuzzy metric spaces, Fixed Point Theory Appl., 3(1)
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(2005), 257{265.
47
[27] W. Rudin, Functional analysis, McGraw-Hill, New York, 1973.
48
[28] A. Sapena, A contribution to the study of fuzzy metric spaces, Appl. Gen. Topol., 2(1) (2001),
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[29] P. Veeramani, Best approximation in fuzzy metric spaces, J. Fuzz. Math., 9(1) (2001), 75{80.
50
[30] L. A. Zadeh, Fuzzy sets,Inform Control., 8(2) (1965), 338{353.
51
ORIGINAL_ARTICLE
Persian-translation vol.13, no.2
http://ijfs.usb.ac.ir/article_2630_64fcd74af2b71ca479d7c69999b15d9a.pdf
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162
171
10.22111/ijfs.2016.2630