ORIGINAL_ARTICLE
Cover vol. 9, no.1, February 2012
http://ijfs.usb.ac.ir/article_2816_9276302627a083223f091bfc721de91a.pdf
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ORIGINAL_ARTICLE
APPLICATION OF TABU SEARCH FOR SOLVING THE
BI-OBJECTIVE WAREHOUSE PROBLEM IN
A FUZZY ENVIRONMENT
The bi-objective warehouse problem in a crisp environment is often not eective in dealing with the imprecision or vagueness in the values of the problem parameters. To deal with such situations, several researchers have proposed that the parameters be represented as fuzzy numbers. We describe a new algorithm for fuzzy bi-objective warehouse problem using a ranking function followed by an application of tabu search. The method is illustrated on a numerical example, demonstrating the eectiveness of the tabu search method. Numerical results are compared for both fuzzy and crisp versions of the problem.
http://ijfs.usb.ac.ir/article_221_2400536ae1bcebd857003631acf8ca86.pdf
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10.22111/ijfs.2012.221
Trapezoidal fuzzy numbers
Bi-objective warehouse problem
Ecient
solution
Tabu search
Anila
Gupta
anilasingal@gmail.com
true
1
School of Mathematics and Computer Applications, Thapar Univer-
sity, Patiala-147004, India
School of Mathematics and Computer Applications, Thapar Univer-
sity, Patiala-147004, India
School of Mathematics and Computer Applications, Thapar Univer-
sity, Patiala-147004, India
LEAD_AUTHOR
Amit
Kumar
amit rs iitr@yahoo.com
true
2
School of Mathematics and Computer Applications, Thapar University,
Patiala-147004, India
School of Mathematics and Computer Applications, Thapar University,
Patiala-147004, India
School of Mathematics and Computer Applications, Thapar University,
Patiala-147004, India
AUTHOR
Mahesh
Kumar Sharma
mksharma@thapar.edu
true
3
School of Mathematics and Computer Applications, Thapar
University, Patiala-147004, India
School of Mathematics and Computer Applications, Thapar
University, Patiala-147004, India
School of Mathematics and Computer Applications, Thapar
University, Patiala-147004, India
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ORIGINAL_ARTICLE
FUZZY GRAVITATIONAL SEARCH ALGORITHM AN
APPROACH FOR DATA MINING
The concept of intelligently controlling the search process of gravitational search algorithm (GSA) is introduced to develop a novel data mining technique. The proposed method is called fuzzy GSA miner (FGSA-miner). At first a fuzzy controller is designed for adaptively controlling the gravitational coefficient and the number of effective objects, as two important parameters which play major roles on search process of GSA. Then the improved GSA (namely Fuzzy-GSA) is employed to construct a novel data mining algorithm for classification rule discovery from reference data sets. Extensive experimental results on different benchmarks and a practical pattern recognition problem with nonlinear, overlapping class boundaries and different feature space dimensions are provided to show the powerfulness of the proposed method. The comparative results illustrate that performance of the proposed FGSA-miner considerably outperforms the standard GSA. Also it is shown that the performance of the FGSA-miner is comparable to, sometimes better than those of the CN2 (a traditional data mining method) and similar approach which have been designed based on other swarm intelligence algorithms (ant colony optimization and particle swarm optimization) and evolutionary algorithm (genetic algorithm).
http://ijfs.usb.ac.ir/article_223_8aafe00e6254010a39a49144e87459eb.pdf
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10.22111/ijfs.2012.223
Gravitational search algorithm
Fuzzy controller
Data mining
Rule
based classifier
Seyed Hamid
Zahiri
hzahiri@@birjand.ac.ir
true
1
Department of Electrical Engineering, Faculty of Engineering,
Birjand University, Birjand, Iran
Department of Electrical Engineering, Faculty of Engineering,
Birjand University, Birjand, Iran
Department of Electrical Engineering, Faculty of Engineering,
Birjand University, Birjand, Iran
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ORIGINAL_ARTICLE
A NEW APPROACH TO STABILITY ANALYSIS OF FUZZY
RELATIONAL MODEL OF DYNAMIC SYSTEMS
This paper investigates the stability analysis of fuzzy relational dynamic systems. A new approach is introduced and a set of sufficient conditions is derived which sustains the unique globally asymptotically stable equilibrium point in a first-order fuzzy relational dynamic system with sumproduct fuzzy composition. This approach is also investigated for other types of fuzzy relational composition.
http://ijfs.usb.ac.ir/article_224_c42486dad4820bcdd105fb3f70d965ff.pdf
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10.22111/ijfs.2012.224
Fuzzy relational dynamic system (FRDS)
Fuzzy relational model
(FRM)
Linguistic stability analysis
Fuzzy relational stability
Arya
Aghili Ashtiani
arya.aghili@aut.ac.ir
true
1
Department of Electrical Engineering, Amirkabir University
of Technology (AUT), P. O. Box 15914, Tehran, Iran
Department of Electrical Engineering, Amirkabir University
of Technology (AUT), P. O. Box 15914, Tehran, Iran
Department of Electrical Engineering, Amirkabir University
of Technology (AUT), P. O. Box 15914, Tehran, Iran
LEAD_AUTHOR
Sayyed Kamaloddin
Yadavar Nikravesh
nikravsh@aut.ac.ir
true
2
Department of Electrical Engineering, Amirk-
abir University of Technology (AUT), P. O. Box 15914, Tehran, Iran
Department of Electrical Engineering, Amirk-
abir University of Technology (AUT), P. O. Box 15914, Tehran, Iran
Department of Electrical Engineering, Amirk-
abir University of Technology (AUT), P. O. Box 15914, Tehran, Iran
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1
systems, Fuzzy Sets and Systems, 154 (2005), 157-181.
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6
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7
on smooth fuzzy norms, Soft Computing, 14(6) (2010), 545-557.
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ORIGINAL_ARTICLE
ON SOLUTION OF A CLASS OF FUZZY BVPs
This paper investigates the existence and uniqueness of solutions to rst-order nonlinear boundary value problems (BVPs) involving fuzzy dif- ferential equations and two-point boundary conditions. Some sucient condi- tions are presented that guarantee the existence and uniqueness of solutions under the approach of Hukuhara dierentiability.
http://ijfs.usb.ac.ir/article_225_64b3d7d1d93425ef5d2a6e2adb0af34c.pdf
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10.22111/ijfs.2012.225
Fuzzy numbers
Fuzzy dierential equations
Boundary value
problems
Omid
Solaymani Fard
osfard@du.ac.ir, omidsfard@gmail.com
true
1
School of Mathematics and Computer Science, Damghan Uni-
versity, Damghan, Iran
School of Mathematics and Computer Science, Damghan Uni-
versity, Damghan, Iran
School of Mathematics and Computer Science, Damghan Uni-
versity, Damghan, Iran
LEAD_AUTHOR
Amin
Esfahani
amin@impa.br, esfahani@du.ac.ir
true
2
School of Mathematics and Computer Science, Damghan University,
Damghan, Iran
School of Mathematics and Computer Science, Damghan University,
Damghan, Iran
School of Mathematics and Computer Science, Damghan University,
Damghan, Iran
AUTHOR
Ali
Vahidian Kamyad
avkamyad@math.um.ac.ir
true
3
Department of Mathematics, Ferdowsi University of Mashhad,
Mashhad, Iran
Department of Mathematics, Ferdowsi University of Mashhad,
Mashhad, Iran
Department of Mathematics, Ferdowsi University of Mashhad,
Mashhad, Iran
AUTHOR
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fuzzy technology in medicine and healthcare, Fuzzy Sets and Systems, 120 (2001), 331-349.
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[2] T. Allahviranloo and M. A. Kermani, Numerical methods for fuzzy linear partial dierential
3
equations under new denition for derivative, Iranian Journal of Fuzzy Systems, 7(3) (2010),
4
[3] S. Bandyopadhyay, An ecient technique for superfamily classication of amino acid se-
5
quences: feature extraction, fuzzy clustering and prototype selection, Fuzzy Sets and Systems,
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152 (2005), 5-16.
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[4] S. Barro and R. Marn, Fuzzy logic in medicine, Heidelberg: Physica-Verlag, 2002.
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[5] B. Bede, Note on "Numerical solutions of fuzzy dierential equations by predictor-corrector
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method", Information Sciences, 178 (2008), 1917-1922.
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[6] B. Bede and S. G. Gal, Generalizations of the dierentiability of fuzzy number valued func-
11
tions with applications to fuzzy dierential equation, Fuzzy Sets and Systems, 151 (2005),
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(2000), 43-54.
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(2005), 139-58.
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Chaos, Solitions and Fractals, 38 (2008), 112-119.
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equations, IEEE Trans Fuzzy Syst., 7 (1999), 734-740.
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lems, Iranian Journal of Fuzzy Systems, 8(1) (2011), 49-63.
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Nonlinear Anal., 58 (2004), 351-358.
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tems, 10 (1983), 87-99.
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Springer-Verlag, New York, 2003.
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namics models, Math. Comput. Model., 37 (2003), 651-658.
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[20] M. Guo and R. Li, Impulsive functional dierential inclusions and fuzzy population models,
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Fuzzy Sets and Systems, 138 (2003), 601-615.
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[21] C. M. Helgason and T. H. Jobe, The fuzzy cube and causal ecacy: representation of con-
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comitant mechanisms in stroke, Neural Networks, 11 (1998), 549-555.
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(1990), 389-396.
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Systems, 111 (2000), 351-356.
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[28] M. S. El Naschie, On a fuzzy Khler manifold which is consistent with the two slit experiment,
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equations subject to boundary value conditions, AIP Conf. Proc., 1124 (2009), 264-273.
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Intell. Med., 27 (2003), 81-101.
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dierential equations, Nonlinear Anal., 54 (2003), 405-415.
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tions, Fuzzy Sets and Systems, 159(11) (2008), 1384-1409.
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73
and Systems, 125 (2002), 231-237.
74
ORIGINAL_ARTICLE
SECURING INTERPRETABILITY OF FUZZY MODELS FOR
MODELING NONLINEAR MIMO SYSTEMS USING
A HYBRID OF EVOLUTIONARY ALGORITHMS
In this study, a Multi-Objective Genetic Algorithm (MOGA) is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear Multi-input Multi-output (MIMO) systems. In the process of non- linear system identi cation, structure selection, parameter estimation, model performance and model validation are important objectives. Furthermore, se- curing low-level and high-level interpretability requirements of fuzzy models is especially a complicated task in case of modeling nonlinear MIMO systems. Due to these multiple and conicting objectives, MOGA is applied to yield a set of candidates as compact, transparent and valid fuzzy models. Also, MOGA is combined with a powerful search algorithm namely Dierential Evolution (DE). In the proposed algorithm, MOGA performs the task of membership function tuning as well as rule base identi cation simultaneously while DE is utilized only for linear parameter identi cation. Practical applicability of the proposed algorithm is examined by two nonlinear system modeling prob- lems used in the literature. The results obtained show the eectiveness of the proposed method.
http://ijfs.usb.ac.ir/article_226_cc28b7c778fbedd749288307752c17c8.pdf
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77
10.22111/ijfs.2012.226
Multi-objective
Evolutionary
Fuzzy identication
Compact
Inter-
pretability
Mojtaba
Eftekhari
m.eftekhari59@gmail.com
true
1
Faculty of Islamic Azad University, Sirjan branch, ,Sirjan, Ker-
man, Iran
Faculty of Islamic Azad University, Sirjan branch, ,Sirjan, Ker-
man, Iran
Faculty of Islamic Azad University, Sirjan branch, ,Sirjan, Ker-
man, Iran
AUTHOR
Mahdi
Eftekhari
m.eftekhari@uk.ac.ir
true
2
Department of Computer Engineering, School of Engineering,
Shahid Bahonar University of Kerman, Kerman, Iran
Department of Computer Engineering, School of Engineering,
Shahid Bahonar University of Kerman, Kerman, Iran
Department of Computer Engineering, School of Engineering,
Shahid Bahonar University of Kerman, Kerman, Iran
LEAD_AUTHOR
Maryam
Majidi
majena67@yahoo.com
true
3
Department of Computer Engineering, School of Engineering, Shahid
Bahonar University of Kerman, Kerman, Iran
Department of Computer Engineering, School of Engineering, Shahid
Bahonar University of Kerman, Kerman, Iran
Department of Computer Engineering, School of Engineering, Shahid
Bahonar University of Kerman, Kerman, Iran
AUTHOR
Hossein
Nezamabadi pour
nezam h@yahoo.com
true
4
Department of Electrical Engineering, School of Engi-
neering, Shahid Bahonar University of Kerman, Kerman, Iran
Department of Electrical Engineering, School of Engi-
neering, Shahid Bahonar University of Kerman, Kerman, Iran
Department of Electrical Engineering, School of Engi-
neering, Shahid Bahonar University of Kerman, Kerman, Iran
AUTHOR
[1] J. Abonyi, Fuzzy Model Identication for Control, Birkhauser, Boston, 2003.
1
[2] K. Debs, Multio-bjective optimization Using Evolutionary Algorithms, John Wiley and Son
2
Ltd, 2001.
3
[3] B. L. R. De Moor and ed., DaISy: Database for the Identication of Systems,
4
Department of Electrical Engineering, ESAT/SISTA, K. U. Leuven, Belgium, URL:
5
http://www.esat.kuleuven.ac.be/sista/daisy/.
6
[4] V. De Oliveira, Semantic constraints for membership function optimization, IEEE Trans.
7
SMC-A, 29(1) (1999), 128-138.
8
[5] M. Eftekhari, S. D. Katebi, M. Karimi and A. H. Jahanmiri, Eliciting transparent fuzzy model
9
using dierential evolution, Applied Soft Computing, 8 (2008), 466-476.
10
[6] M. Eftekhari and S. D. Katebi, Extracting compact fuzzy rules for nonlinear system modeling
11
using subtractive clustering, GA and unscented lter, Applied Mathematical Modelling, 32
12
(2008), 2634-2651.
13
[7] B. Feil, J. Abonyi, J. Madar, S. Nemeth and P. A rva, Identication and analysis of MIMO
14
systems based on clustering algorithm, Acta Agraria Kaposvariensis, 8(3) (2004), 191-203.
15
[8] C. M. Fonseca and P. J. Fleming, Multi-objective optimization and multiple constraint han-
16
dling with evolutionary algorithms-part I: application example, IEEE Trans. Syst. Man and
17
Cybernetics, 28(1) (1998), 26-37.
18
[9] C. M. Fonseca and P. J. Fleming, Multi-objective optimization and multiple constraint han-
19
dling with evolutionary algorithms-part II: a unied formulation, IEEE Trans. Syst. Man and
20
Cybernetics, 28(1) (1998), 38-47.
21
[10] M. J. Gacto, R. Alcala and F. Herrera, Integration of an Index to Preserve the Semantic
22
Interpretability in the Multiobjective Evolutionary Rule Selection and Tuning of Linguistic
23
Fuzzy Systems, IEEE Transactions on Fuzzy Systems, 8(3) (2010), 515-531.
24
[11] S. Y. Ho, H. M. Chen, S. J. Ho and T. K. Cehn, Design of accurate classiers with a compact
25
fuzzy rule base using an evolutionary scatter partition of feature space, IEEE Tans. Systems,
26
Man and Cybernetics, Part B: Cybernetics, 34(2) (2004), 1031-1044.
27
[12] W. H. Ho, J. H. Chou and C. Y. Guo, Parameter identication of chaotic systems using
28
improved dierential evolution algorithm, Nonlinear Dynamics, 61 (2010), 29-41.
29
[13] A. Homaifar and E. McCormick, Simultaneous design of membership functions and rule sets
30
for fuzzy controllers using genetic algorithms, IEEE Trans. Fuzzy Syst., 3 (1995), 129-139.
31
[14] H. Ishibuchi, Multiobjective genetic fuzzy systems: Review and future research directions,
32
Proc. of IEEE InternationalConference on Fuzzy Systems, London, UK, July 23-26, (2007)
33
[15] C. Z. Janikow, A knowledge intensive genetic algorithm for supervised learning, Machine
34
Learning, 13 (1993), 198-228.
35
[16] L. Ljung, System identication toolbox: user's guide, The MathWorks, 2004.
36
[17] S. Medasani, J. Kim and R. Krishnapuram, An overview of membership function generation
37
techniques for pattern recognition, Int. J. Approx. Reasoning, 19(3-4) (1998), 391-417.
38
[18] O. Nelles, Nonlinear System Identication, Springer-Verlag, Berlin Heidelberg, 2001.
39
[19] A. Riid and E. Rustern, Interpretability improvement of fuzzy systems: reducing the number
40
of unique singletons in zeroth order Takagi-Sugeno systems, Proceedings of (2010) IEEE
41
International Conference on Fuzzy Systems, Barcelona, Spain, (2010), 2013-2018.
42
[20] K. Rodriguez-vazquez, Multiobjective evolutionary algorithms in non-linear system identi-
43
cation. PhD thesis, Department of Automatic Control and Systems Engineering, The Uni-
44
versity of Sheeld, 1999.
45
[21] R. Storn and K. Price, Dierential evolution-a simple and ecient adaptive scheme or global
46
optimization over continuous spaces, technical report TR-95-012, International Computer
47
Science Institute, Berkley, 1995.
48
[22] R. Storn and K. Price, Dierential evolution a simple and ecient heuristic for global opti-
49
mization over continuous spaces, J. Global Optim. 11 (1997), 341-359.
50
[23] J. T. Tsai, J. H. Chou and T. K. Liu, Tuning the structure and parameters of a neural
51
network by using hybrid Taguchi-genetic algorithm, IEEE Trans. on Neural Networks, 17
52
(2006), 69-80.
53
[24] H. Wang, S. Kwong, Y. Jin, W. Wei, and K. F. Man, Multi-objective hierarchical genetic
54
algorithm for interpretable fuzzy rule-based knowledge extraction, Fuzzy Sets and Systems,
55
149 (2005), 149-186.
56
[25] H.Wang, S. Kwong, Y. Jin, W.Wei and K. F. Man, Agent-based evolutionary approach for in-
57
terpretable rule-based knowledge extraction, IEEE Trans. on Systems, Man and Cybernetics-
58
Part C, 35 (2005), 143-155.
59
[26] S. M. Zhou and J. Q. Gan, Low-level interpretability and high-level interpretability: a unied
60
view of data-driven interpretable fuzzy system modeling, Fuzzy Sets and Systems, 159 (2008),
61
3091-3131.
62
[27] S. M. Zhou and J. Q. Gan, Extracting Takagi-Sugeno fuzzy rules with interpretable sub-
63
models via regularization of linguistic modiers, IEEE Transactions on Knowledge and Data
64
Engineering, 21(8) (2009), 1191-1204.
65
ORIGINAL_ARTICLE
ESTIMATORS BASED ON FUZZY RANDOM VARIABLES AND
THEIR MATHEMATICAL PROPERTIES
In statistical inference, the point estimation problem is very crucial and has a wide range of applications. When, we deal with some concepts such as random variables, the parameters of interest and estimates may be reported/observed as imprecise. Therefore, the theory of fuzzy sets plays an important role in formulating such situations. In this paper, we rst recall the crisp uniformly minimum variance unbiased (UMVU) and Bayesian estimators and then develop the concept of fuzzy estimators for fuzzy parameters based on fuzzy random variables.
http://ijfs.usb.ac.ir/article_227_b17c576a5e5de627e505d9f09b0d933c.pdf
2012-02-11T11:23:20
2017-11-22T11:23:20
79
95
10.22111/ijfs.2012.227
Fuzzy random variable
Fuzzy parameter
Signed distance
L2- metric
Fuzzy estimator
Fuzzy unbiased estimator
Fuzzy sufficient estimator
Fuzzy risk function
M. G.
Akbari
mga13512@yahoo.com
true
1
Department of Statistics, Faculty of Sciences, University of Birjand,
Southern Khorasan, Birjand
Department of Statistics, Faculty of Sciences, University of Birjand,
Southern Khorasan, Birjand
Department of Statistics, Faculty of Sciences, University of Birjand,
Southern Khorasan, Birjand
LEAD_AUTHOR
M.
Khanjari Sadegh
g_z_akbari@yahoo.com
true
2
Department of Statistics, Faculty of Sciences, University of
Birjand, Southern Khorasan, Birjand
Department of Statistics, Faculty of Sciences, University of
Birjand, Southern Khorasan, Birjand
Department of Statistics, Faculty of Sciences, University of
Birjand, Southern Khorasan, Birjand
AUTHOR
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1
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H. Hong-Zhong, J. Z. Ming and S. Zhan-Quan, {it Bayesian
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O. Hryniewicz, {it Possibilities approach to the Bayes statistical
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Methods in Probability, Statistics and Data Analysis. Physica
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W. N$ddot{a}$ther, {it Regression with fuzzy data},
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83
ORIGINAL_ARTICLE
A NEW METHOD TO REDUCE TORQUE RIPPLE IN
SWITCHED RELUCTANCE MOTOR USING
FUZZY SLIDING MODE
This paper presents a new control structure to reduce torque ripple in switched reluctance motor. Although SRM possesses many advantages in motor structure, it suers from large torque ripple that causes some problems such as vibration and acoustic noise. In this paper another control loop is added and torque ripple is de ned as an objective function. By using fuzzy sliding mode strategy, the DC link voltage is adjusted to optimize the objective function. Simulation results have demonstrated the proposed control method.
http://ijfs.usb.ac.ir/article_228_eda29a4d8485fce43ad3b0b5e35789ed.pdf
2012-02-11T11:23:20
2017-11-22T11:23:20
97
108
10.22111/ijfs.2012.228
Fuzzy sliding control
Switched Reluctance Motor
Torque ripple re-
duction
S. R.
Mousavi-Aghdam
rmousavi@tabrizu.ac.ir
true
1
Faculty of electrical and computer engineering, University
of Tabriz, Tabriz, Iran
Faculty of electrical and computer engineering, University
of Tabriz, Tabriz, Iran
Faculty of electrical and computer engineering, University
of Tabriz, Tabriz, Iran
LEAD_AUTHOR
M. B. B.
Sharifian
sharifian@tabrizu.ac.ir
true
2
Faculty of electrical and computer engineering, University of
Tabriz, Tabriz, Iran
Faculty of electrical and computer engineering, University of
Tabriz, Tabriz, Iran
Faculty of electrical and computer engineering, University of
Tabriz, Tabriz, Iran
AUTHOR
M. R.
Banaei
m.banaei@azaruniv.edu
true
3
Department of electrical engineering, Faculty of engineering, azarbai-
jan, University of tarbiat moallem, Tabriz, Iran
Department of electrical engineering, Faculty of engineering, azarbai-
jan, University of tarbiat moallem, Tabriz, Iran
Department of electrical engineering, Faculty of engineering, azarbai-
jan, University of tarbiat moallem, Tabriz, Iran
AUTHOR
[1] M. R. Akbarzadeh-T and R. Shahnazi, Direct adaptive fuzzy PI sliding mode control of system
1
with unknown but bounded disturbances, Iranian Journal of Fuzzy Systems, 3(2) (2006), 33-
2
[2] D. Cajander and H. Le-Huy, Design and optimization of a torque controller for a switched
3
reluctance motor drive for electric vehicles by simulation, Mathematics and Computers in
4
Simulation, Elsevier, 71 (2006), 333-344.
5
[3] J. Y. Chai and C. M. Liaw, Reduction of speed ripple and vibration for switched reluctance
6
motor drive via intelligent current proling, IET Electric Power Applications, (2010), 380-
7
[4] N. Inanc and V. Ozbulur, Torque ripple minimization of a switched reluctance motor by
8
using continuous sliding mode control technique, Electric power systems research, Elsevier,
9
66 (2003), 241-251.
10
[5] G. John and A. R. Eastham, Speed control of switched reluctance motor using sliding mode
11
control strategy, IEEE International Conference on Industrial Technology, 1 (1995), 263-270.
12
[6] H. Khorashadi-Zadeh and M. R. Aghaebrahimi, A neuro-fuzzy technique for discrimination
13
between internal faults and magnetizing inrush currents in transformers, Iranian Journal of
14
Fuzzy Systems, 2(2) (2005), 45-57.
15
[7] J. Li, X. Song and Y. Cho, Comparison of 12/8 and 6/4 switched reluctance motor: noise
16
and vibration aspects, IEEE Trans. Magn., 44(11) (2008), 4131-4134.
17
[8] J. Li and Y. Cho, Investigation into reduction of vibration and acoustic noise in switched
18
reluctance motors in radial force excitation and frame transfer function aspects, IEEE Trans.
19
Magn., 45(10) (2009), 4664-4667.
20
[9] M. A. A. Morsy, M. Said, A. Moteleb and H. T. Dorrah, Design and implementation of
21
fuzzy sliding mode controller for switched reluctance motor, IEEE International Conference
22
on Industrial Technology, (2008), 1-6.
23
[10] N. Nelvaganesan, D. Raja and S. Srinivasan, Fuzzy based fault detection and control for 6/4
24
switched reluctance motor, Iranian Journal of Fuzzy Systems, 45(1) (2007), 37-51.
25
[11] H. Rouhani, C. Lucas, R. Mohammadi Milasi and M. Nikkhah bahrami, Fuzzy sliding mode
26
control applied to low noise switched reluctance motor control, International Conference on
27
Control and Automation (ICCA), 1 (2005), 325-329.
28
[12] W. Shang, S. Zhao, Y. Shen and Z. Qi, A sliding mode
29
ux-linkage controller with integral
30
compensation for switched reluctance motor, IEEE Trans. Magn., 45(9) (2009), 3322-3328.
31
[13] B. Singh, V. Kumar Sharma and S. S. Murthy, Comparative study of PID, sliding mode and
32
fuzzy logic controllers for four quadrant operation of switched reluctance motor, International
33
Conference on Power Electronic Drives and Energy Systems for Industrial Growth, 1 (1998),
34
[14] H. Vasquez and J. K. Parker, A new simplied mathematical model for a switched reluctance
35
motor in a variable speed pumping application, Mechatronics, Elsevier, 14 (2004), 1055-1068.
36
[15] K. Vijayakumar, R. Karthikeyan, S. Paramasivam, R. Arumugam and K. N. Srinivas,
37
Switched reluctance motor modeling, design, simulation, and analysis: a comprehensive re-
38
view, IEEE Trans. Magn., 44(12) (2008), 4605-4617.
39
[16] Y. Wang, A novel fuzzy controller for switched reluctance motor drive, Second IEEE Inter-
40
national Conference on Information and Computing Science, 2 (2009), 55-58.
41
ORIGINAL_ARTICLE
FUZZY SOFT MATRIX THEORY AND ITS APPLICATION IN
DECISION MAKING
In this work, we define fuzzy soft ($fs$) matrices and theiroperations which are more functional to make theoretical studies inthe $fs$-set theory. We then define products of $fs$-matrices andstudy their properties. We finally construct a $fs$-$max$-$min$decision making method which can be successfully applied to theproblems that contain uncertainties.
http://ijfs.usb.ac.ir/article_229_b6d292816ccde2e91d91920714cb6245.pdf
2012-02-11T11:23:20
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119
10.22111/ijfs.2012.229
Fuzzy soft sets
Fuzzy soft matrix
Products of fuzzy soft matrices
Fuzzy soft max-min decision making
Naim
Cagman
ncagman@gop.edu.tr
true
1
Department of Mathematics, Faculty of Arts and Sciences, Gazios-
manpasa University, 60250 Tokat, Turkey
Department of Mathematics, Faculty of Arts and Sciences, Gazios-
manpasa University, 60250 Tokat, Turkey
Department of Mathematics, Faculty of Arts and Sciences, Gazios-
manpasa University, 60250 Tokat, Turkey
LEAD_AUTHOR
Serdar
Enginoglu
serdarenginoglu@gop.edu.tr
true
2
Department of Mathematics, Faculty of Arts and Sciences, Gazios-
manpasa University, 60250 Tokat, Turkey
Department of Mathematics, Faculty of Arts and Sciences, Gazios-
manpasa University, 60250 Tokat, Turkey
Department of Mathematics, Faculty of Arts and Sciences, Gazios-
manpasa University, 60250 Tokat, Turkey
AUTHOR
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ORIGINAL_ARTICLE
FUZZY LINEAR REGRESSION BASED ON
LEAST ABSOLUTES DEVIATIONS
This study is an investigation of fuzzy linear regression model for crisp/fuzzy input and fuzzy output data. A least absolutes deviations approach to construct such a model is developed by introducing and applying a new metric on the space of fuzzy numbers. The proposed approach, which can deal with both symmetric and non-symmetric fuzzy observations, is compared with several existing models by three goodness of t criteria. Three well-known data sets including two small data sets as well as a large data set are employed for such comparisons.
http://ijfs.usb.ac.ir/article_230_d59e96e289e06159aecc01cdcd61a9dd.pdf
2012-02-11T11:23:20
2017-11-22T11:23:20
121
140
10.22111/ijfs.2012.230
Fuzzy regression
Least absolutes deviations
Metric on fuzzy numbers
Similarity measure
Goodness of fit
S. M.
Taheri
sm_taheri@yahoo.com
true
1
Department of Mathematical Sciences, Isfahan University of Technology, Isfahan 84156-83111, Iran and Department of Statistics, School of Mathematical
Sciences, Ferdowsi University of Mashhad, Mashhad, Iran
Department of Mathematical Sciences, Isfahan University of Technology, Isfahan 84156-83111, Iran and Department of Statistics, School of Mathematical
Sciences, Ferdowsi University of Mashhad, Mashhad, Iran
Department of Mathematical Sciences, Isfahan University of Technology, Isfahan 84156-83111, Iran and Department of Statistics, School of Mathematical
Sciences, Ferdowsi University of Mashhad, Mashhad, Iran
LEAD_AUTHOR
M.
Kelkinnama
m_ kelkinnama@yahoo.com
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 linear regression model}, Iranian Journal of Fuzzy Systems,
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with fuzzy data}, Fuzzy Sets and Systems, {bf 158} (2007),
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2169-2188.
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the IEEE Int. Conf. Fuzzy Syst. (FUZZ-IEEE 2011), Taipei, Taiwan, (2011), 2859-2863.
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Fibers and Polymers, to appear.
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ORIGINAL_ARTICLE
TRANSPORT ROUTE PLANNING MODELS BASED
ON FUZZY APPROACH
Transport route planning is one of the most important and frequent activities in supply chain management. The design of information systems for route planning in real contexts faces two relevant challenges: the complexity of the planning and the lack of complete and precise information. The purpose of this paper is to nd methods for the development of transport route planning in uncertainty decision making contexts. The paper uses an approximation that integrates a speci c fuzzy-based methodology from Soft Computing. We present several fuzzy optimization models that address the imprecision and/or exibility of some of its components. These models allow transport route planning problems to be solve with the help of metaheuristics in a concise way. A simple numerical example is shown to illustrate this approach.
http://ijfs.usb.ac.ir/article_231_7ebf6519fbcb9484fdaa9a6e8d293100.pdf
2012-02-11T11:23:20
2017-11-22T11:23:20
141
158
10.22111/ijfs.2012.231
Fuzzy optimization
Route planning
Soft computing
Julio
Brito
jbrito@ull.es
true
1
I. U. D. R., University of La Laguna, E-38271 Tenerife, Spain
I. U. D. R., University of La Laguna, E-38271 Tenerife, Spain
I. U. D. R., University of La Laguna, E-38271 Tenerife, Spain
LEAD_AUTHOR
Jose A.
Moreno
jamoreno@ull.es
true
2
I. U. D. R., University of La Laguna, E-38271 Tenerife, Spain
I. U. D. R., University of La Laguna, E-38271 Tenerife, Spain
I. U. D. R., University of La Laguna, E-38271 Tenerife, Spain
AUTHOR
Jose L.
Verdegay
verdegay@decsai.ugr.es
true
3
Department C. C. I. A., University of Granada, E-18071 Granada,
Spain
Department C. C. I. A., University of Granada, E-18071 Granada,
Spain
Department C. C. I. A., University of Granada, E-18071 Granada,
Spain
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ORIGINAL_ARTICLE
Persian-translation vol. 9, no.1, February 2012
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