ORIGINAL_ARTICLE
Cover vol. 8, no. 3, october 2011
http://ijfs.usb.ac.ir/article_2870_00c38aa58385d4e27951d5fc93c237ec.pdf
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10.22111/ijfs.2011.2870
ORIGINAL_ARTICLE
A SOLUTION TO AN ECONOMIC DISPATCH PROBLEM BY A
FUZZY ADAPTIVE GENETIC ALGORITHM
In practice, obtaining the global optimum for the economic dispatch {bf (ED)}problem with ramp rate limits and prohibited operating zones is presents difficulties. This paper presents a new andefficient method for solving the economic dispatch problem with non-smooth cost functions using aFuzzy Adaptive Genetic Algorithm (FAGA). The proposed algorithm deals with the issue ofcontrolling the exploration and exploitation capabilities of a heuristic search algorithm in whichthe real version of Genetic Algorithm (RGA) is equipped with a Fuzzy Logic Controller (FLC)which can efficiently explore and exploit optimum solutions. To validate the results obtainedby the proposed FAGA, it is compared with a Real Genetic Algorithm (RGA). Moreover, the resultsobtained by FAGA and RGA are also compared with those obtained by other approaches reported in the literature.It was observed that the FAGA outperforms the other methods in solving the power system economicload dispatch problem in terms of quality, as well as convergence and success rates.
http://ijfs.usb.ac.ir/article_283_e8f1a7a622ced9b8eb173f4167349fec.pdf
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10.22111/ijfs.2011.283
Economic dispatch
Genetic algorithm
%Fuzzy adaptive genetic algorithm
Non-smooth cost functions
H.
Nezamabadi-pour
nezam@mail.uk.ac.ir
true
1
Electrical Engineering Department, Shahid Bahonar University
of Kerman, Kerman, Iran
Electrical Engineering Department, Shahid Bahonar University
of Kerman, Kerman, Iran
Electrical Engineering Department, Shahid Bahonar University
of Kerman, Kerman, Iran
LEAD_AUTHOR
S.
Yazdani
sajjad.yazdani@gmail.com
true
2
Electrical Engineering Department, Shahid Bahonar University of Kerman,
Kerman, Iran
Electrical Engineering Department, Shahid Bahonar University of Kerman,
Kerman, Iran
Electrical Engineering Department, Shahid Bahonar University of Kerman,
Kerman, Iran
AUTHOR
M. M.
Farsangi
mmaghfoori@mail.uk.ac.ir
true
3
Electrical Engineering Department, Shahid Bahonar University of
Kerman, Kerman, Iran
Electrical Engineering Department, Shahid Bahonar University of
Kerman, Kerman, Iran
Electrical Engineering Department, Shahid Bahonar University of
Kerman, Kerman, Iran
AUTHOR
M.
Neyestani
mehdi2594@yahoo.com
true
4
Electrical Engineering Department, Shahid Bahonar University of
Kerman, Kerman, Iran
Electrical Engineering Department, Shahid Bahonar University of
Kerman, Kerman, Iran
Electrical Engineering Department, Shahid Bahonar University of
Kerman, Kerman, Iran
AUTHOR
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1
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2
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climbing}, Evolutionary Computation, {bf 12}textbf{(3)} (2004), 273-302.
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F. Herrera and M. Lozano, {it Adaptive fuzzy operators based on coevolution with fuzzy
28
behaviours}, IEEE Transaction on Evolutionary Computation, {bf 5}textbf{(2)} (2001), 1-18.
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bibitem{Herrera:FAGA}
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F. Herrera and M. Lozano, {it Fuzzy adaptive genetic algorithm: design, taxonomy, and future
31
directions}, Soft Computing, {bf 7}textbf{} (2003), 545-562.
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33
N. Krasnogor and J. Smith, {it A tutorial for competent memetic algorithms: model, taxonomy and
34
design issues}, IEEE Trans. Evol. Comput., {bf 9}textbf{(5)} (2005), 474-488.
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36
K. Y. Lee, A. Sode-Yome and J. H. Park, {it Adaptive Hopfield neural network for economic load
37
dispatch}, IEEE Trans. Power Syst., {bf 13}textbf{(2)} (1998), 519-526.
38
bibitem{Lin:TSforED}
39
W. M. Lin, F. S. Cheng and M. T. Tsay, {it An improved tabu search for economic dispatch with
40
multiple minima}, IEEE Trans. Power Syst., {bf 17}textbf{(1)} (2002), 108-112.
41
bibitem{Mehdizadeh:H-PSO}
42
E. Mehdizadeh, S. Sadi-nezhad and R. Tavakoli-moghaddam, {it Optimaization of fuzzy clastering
43
criteria by a hybrid PSO and fuzzy c--means clustering algorithm}, Iranian Journal of Fuzzy
44
Systems, {bf 5}textbf{(3)} (2008), 1-14
45
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46
Memetic Algorithms Home Page, http://www.densis.fee.unicamp.br/$sim$moscato /memetic_ home.
47
bibitem{Mrz:FLandMAforQAP}
48
P. Merz and B. Freisleben, {it Fitness landscape analysis and memetic algorithms for the
49
quadratic assignment problem}, IEEE Trans. Evol. Comput., {bf 4}textbf{(4)} (2000), 337-352.
50
bibitem{Moscato:MA}
51
P. Moscato, {it On evolution, search, optimization, genetic algorithms and martial arts: towards
52
memetic algorithms}, Memetic Algorithms Home Page, http://www. densis. fee. unicamp. br /$sim
53
$moscato /memetic_ home. html.
54
bibitem{Neyestani:MAforED}
55
M. Neyestani, M. M. Farsangi and H. Nezamabadi-pour, {it Memetic algorithm for economic dispatch
56
with nonsmooth cost functions}, Accepted by Iranian Journal of Electrical and Computer Engineering
57
(IJECE) (in Farsi).
58
bibitem{Oreo:ED-POZ}
59
S. O. Orero and M. R. Irving, {it Economic dispatch of generators with prohibited operating
60
zones: a genetic algorithm approach}, Proc. Inst. Elect. Eng. Gen. Transm. Distrib., {bf
61
143}textbf{(6)} (1996), 529-534.
62
bibitem{Park:PSOforED}
63
J. B. Park, K. S. Lee, J. R. Shin and K. Y. Lee, {it A particle swarm optimization for economic
64
dispatch with nonsmooth cost functions}, IEEE Trans. Power Syst., {bf 20}textbf{(1)} (2005), 34
65
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66
E. Rashedi, H. Nezamabadi-pour and S. Saryazdi, {it GSA: a gravitational search algorithm},
67
Information Sciences, {bf 179}textbf{(13)} (2009), 2232-2248.
68
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69
A. I. Selvakumar and K. Thanushkodi, {it A new particle swarm optimization solution to nonconvex
70
economic dispatch problems}, IEEE Trans. Power Syst., {bf 22}textbf{(1)} (2007), 42-51.
71
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N. Sinha, R. Chakrabarti and P. K. Chattopadhyay, {it Evolutionary programming techniques for
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economic load dispatch}, IEEE Trans. Evol. Comput., {bf 7}textbf{(1)} (2003), 83-94.
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T. Sum-im, {it Economic dispatch by ant colony search algorithm}, Proc. IEEE Conference on
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Cybernetics and Intelligent Systems, (2004), 416-421.
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S. K. Wang, J. P. Chiou and C. W. Liu, {it Non-smooth/non-convex economic dispatch by a novel
79
hybrid differential evolution algorithm}, IET Generation Transmission & Distribution, {bf
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1}textbf{(5)} (2007), 793-803.
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units with non-smooth fuel cost functions}, IEEE Trans. Power Syst., {bf 11}textbf{(1)} (1996),
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92
European Congress on Intelligent Techniques and Soft Computing, (1997), 660-664.
93
ORIGINAL_ARTICLE
Fuzzy relations, Possibility theory, Measures of uncertainty, Mathematical modeling.
A central aim of educational research in the area of mathematical modeling and applications is to recognize the attainment level of students at defined states of the modeling process. In this paper, we introduce principles of fuzzy sets theory and possibility theory to describe the process of mathematical modeling in the classroom. The main stages of the modeling process are represented as fuzzy sets in a set of linguistic labels indicating the degree of a student's success in each of these stages. We use the total possibilistic uncertainty on the ordered possibility distribution of all student profiles as a measure of the students' modeling capacities and illustrate our results by application to a classroom experiment.
http://ijfs.usb.ac.ir/article_284_9bab58dbf42b5b3133cbad5825aa1e52.pdf
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10.22111/ijfs.2011.284
Fuzzy relations
Possibility theory
Measures of uncertainty
Mathematical modeling
Michael Gr.
Voskoglou
voskoglou@teipat.gr
true
1
Graduate Technological Educational Institute (T.E.I.),
School of Technological Applications, 263 34 Patras, Greece
Graduate Technological Educational Institute (T.E.I.),
School of Technological Applications, 263 34 Patras, Greece
Graduate Technological Educational Institute (T.E.I.),
School of Technological Applications, 263 34 Patras, Greece
LEAD_AUTHOR
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1
M. Ajello and F. Spagnolo, {it Some experimental observations on common sense and fuzzy logic}, Proceedings of International Conference on Mathematics Education into the 21st Century, Napoli, (2002), 35-39.
2
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3
R. Borroneo Ferri, {it Modeling problems from a cognitive perspective}, In: C. R. Haines, P. Galbraith, W. Bloom, S. Khan, eds., Mathematical Modeling: Education, Engineering and Economics, (ICTMA 12), Horwood Publishing, Chichester, (2007), 260-270.
4
bibitem{Do:2007}
5
H. M. Doer, {it What knowledge do teachers need for teaching mathematics through applications and modeling?}, In: W. Blum, P. L. Galbraith, H. W. Henn and M. Niss, eds., Modeling and Applications in Mathematics Education, Springer, NY, (2007), 69-78.
6
bibitem{EsOli:1997}
7
E. A. Espin and C. M. L. Oliveras, {it Introduction to the use of fuzzy logic in the assessment of mathematics teachers'}, In: A. Gagatsis, ed., Proceedings of the 1$^{st}$ Mediterranean Conference on Mathematics Education, Nicosia, Cyprus, (1997), 107-113.
8
bibitem{GalSti:2001}
9
P. L. Galbraith and G. Stillman, {it Assumptions and context: pursuing their role in modeling activity}, In: J. F. Matos, W. Blum, K. Houston and S. P. Carreira, eds., Modeling and Mathematics Education: Applications in Science and Technology (ICTMA 9), Chichester, (2001), 300-310.
10
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11
C. R. Haines and R. Crouch, {it Remarks on modeling cycle and interpretation of behaviours}, In: R. A. Lesh, P. L. Galbraith, C. R. Haines and A. Harford, eds., Modeling Students' Mathematical Modeling Competencies, (ICTMA13), London, (2010), 145-154.
12
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13
H. L. Huang and G. Shi, {it Robust H1 control for T-S time-varying delay systems with norm bounded uncertainty based on LMI approach}, Iranian Journal of Fuzzy Systems, {bf 6}textbf{(1)} (2009), 1-14.
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15
L. Iliadis and S. Spartalis, {it An intelligent information system for fuzzy additive modeling (hydrological risk application)}, Iranian Journal of Fuzzy Systems, {bf 7}textbf{(1)} (2010), 1-14.
16
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17
N. Javiadian, Y. Maali and N. Mahadavi-Amiri, {it Fuzzy linear programming with grades of satisfaction in constraints}, Iranian Journal of Fuzzy Systems, {bf 6}textbf{(3)} (2009), 17-35.
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I. Kaya and J. Kahraman, {it Fuzzy process capability analyses: an application to teaching processes}, Journal of Intelligent and Fuzzy Systems, {bf 19}textbf{(4-5)} (2008), 259-272.
20
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21
C. Kezi Selva Vijila and P. Kanagasalarathy, {it Intelligent technique of cancelling maternal ECG in FECG extraction}, Iranian Journal of Fuzzy Systems, {bf 5}textbf{(1)} (2008), 27-45.
22
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23
G. J. Klir and T. A. Folger, {it Fuzzy sets, uncertainty and information}, Prentice Hall, London, 1988.
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G. J. Klir and M. J. Wierman, {it Uncertainty-based information: elements of generalized information theory}, Physika-Verlag, Heidelberg, 1998.
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29
S. Meier, {it Identifying modeling tasks}, In: L. Paditz and A. Rogerson, eds., Models in Developing Mathematics Education (MEC21), Dresden, University of Applied Sciences, (2009), 399-403.
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P. K. Nayak and M. Pal, {it Bi-matrix games with intuitionistic fuzzy goals}, In: L. Paditz and A. Rogerson, eds., Iranian Journal of Fuzzy Systems, {bf 7}textbf{(1)} (2010), 65-79.
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40
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M. G. Voskoglou, {it Measuring mathematical model building abilities}, International Journal of Mathematical Education in Science and Technology, {bf 26}textbf{(1)} (1995), 29-35.
44
bibitem{Vo:1996}
45
M. G. Voskoglou, {it An application of ergodic Markov chains to analogical problem solving}, The Mathematics Education (India), {bf XXX}textbf{(2)} (1996), 95-108.
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bibitem{Vo:1999}
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M. G. Voskoglou, {it The process of learning mathematics: a fuzzy set approach}, Heuristics and Didactics of Exact Sciences (Ukraine), {bf 10} (1999), 9-13.
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bibitem{Vo:2006}
51
M. G. Voskoglou, {it The use of mathematical modeling as a tool for learning mathematics}, Quaderni di Ricerca in Didattica (Scienze Mathematiche), {bf 16} (2006), 53-60.
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bibitem{Vo:2007}
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M. G. Voskoglou, {it A stochastic model for the modeling process}, In: C. R. Haines, P. Galbraith, W. Bloom, S. Khan, eds., Mathematical Modeling: Education, Engineering and Economics, (2007), 149-157.
54
bibitem{Vo:2009}
55
M. G. Voskoglou, {it Fuzziness or probability in the process of learning? a general question illustrated by examples from teaching mathematics}, The Journal of Fuzzy Mathematics, International Fuzzy Mathematics Institute (Los Angeles), {bf 17}textbf{(3)} (2009), 697-686.
56
bibitem{Vo:2009a}
57
M. G. Voskoglou, {it Fuzzy sets in case-based reasoning}, In: Y. Chen, H. Deng, D. Zhang and Y. Xiao, eds., Fuzzy Systems and Knowledge Discovery (FSKD 2009), {bf 6} (2009), 252-256.
58
bibitem{Vo:2009b}
59
M. G. Voskoglou, {it A stochastic model for the process of learning}, In: L. Paditz and A. Rogerson, eds., Models in Developing Mathematics Education (MEC21) Dresden, (2009), 565-569.
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bibitem{Vo:2010}
61
M. G. Voskoglou, {it Mathematizing the case-based reasoning process}, In: A. M. Leeland, ed., Case-Based Reasoning: Processes, Suitability and Applications, in press, {bf 6} (2010).
62
ORIGINAL_ARTICLE
Optimal Control with Fuzzy Chance Constraints
In this paper, a model of an optimal control problem with chance constraints is introduced. The parametersof the constraints are fuzzy, random or fuzzy random variables. Todefuzzify the constraints, we consider possibility levels. Bychance-constrained programming the chance constraints are converted to crisp constraints which are neither fuzzy nor stochastic and then the resulting classical optimalcontrol problem with crisp constraints is solved by thePontryagin Minimum Principle and Kuhn-Tucker conditions. The modelis illustrated by two numerical examples.
http://ijfs.usb.ac.ir/article_285_4e09bdb20249d87c4d326aba52f5de42.pdf
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10.22111/ijfs.2011.285
Fuzzy random
variable
Chance-constrained programming
Possibility
level
Saeed
Ramezanzadeh
ramezanzadeh@phd.pnu.ac.ir
true
1
Department of Mathematics, Payame Noor University, Tehran,
Iran and Department of Mathematics, Faculty of Technology, Olum Entezami University,
Tehran, Iran
Department of Mathematics, Payame Noor University, Tehran,
Iran and Department of Mathematics, Faculty of Technology, Olum Entezami University,
Tehran, Iran
Department of Mathematics, Payame Noor University, Tehran,
Iran and Department of Mathematics, Faculty of Technology, Olum Entezami University,
Tehran, Iran
LEAD_AUTHOR
Aghileh
Heydari
a_heidari@pnu.ac.ir
true
2
Department of Mathematics, Payame Noor University, Mashhad,
Iran
Department of Mathematics, Payame Noor University, Mashhad,
Iran
Department of Mathematics, Payame Noor University, Mashhad,
Iran
AUTHOR
D. Chakraborty, {it Redefining chance-constrained programming in
1
fuzzy environment}, FSS, {bf 125} (2002), 327-333.
2
A. Charns and W. Cooper, {it Chance constrained programming}, Management Science,
3
{bf 6 } (1959), 73-79.
4
D. Dubois and H. Prade, {it Ranking fuzzy numbers in the setting of
5
possibility theory}, Information sciences, {bf 30} (1983),
6
N. Javadin, Y. Maali and N. Mahdavi-Amiri, {it Fuzzy linear
7
programming with grades of satisfaction in constraints}, Iranian
8
Journal of Fuzzy Systems, {bf 6(3)} (2009), 17-35.
9
H. Kuakernaak, {it Fuzzy random variables, definitions and
10
theorems}, Information Sciences, {bf 15} (1978), 1-29.
11
B. Liu, {it Fuzzy random chance-constrained programming}, IEEE Transactions on
12
Fuzzy Systems, {bf 9(5)} (2001), 713-720.
13
B. Liu, {it Fuzzy random dependent-chance programming}, IEEE Transactions on
14
Fuzzy Systems, {bf 9(5)} (2001), 721-726.
15
M. K. Maiti and M. Maiti, {it Fuzzy inventory model with two
16
warehouses under possibility constraints}, FSS, {bf 157} (2006),
17
K. Maity and M. Maiti, {it Possibility and necessity constraints and
18
their defuzzification- a multi-item production-inventory
19
scenario via optimal control theory}, European Journal of
20
Operational Research, {bf 177} (2007), 882-896.
21
L. S. Pontryagin and et al., {it The mathematical theory of optimal
22
process}, International Science, New York, 1962.
23
S. Ramezanzadeh, M. Memriani and S. Saati, {it Data envelopment
24
analysis with fuzzy random inputs and outputs: a
25
chance-constrained programming approach}, Iranian Journal of Fuzzy
26
Systems, {bf 2(2)} (2005), 21-31.
27
M. R. Safi, H. R. Maleki and E. Zaeimazad, {it A note on the
28
zimmermann method for solving fuzzy linear programming problems},
29
Iranian Journal of Fuzzy Systems, {bf 4(2)} (2007), 31-45.
30
E. Shivanian, E. Khorram and A. Ghodousian, {it Optimization of
31
linear objective function subject to fuzzy relatin inequalities
32
constraints with max-average composition}, Iranian Journal of
33
Fuzzy Systems, {bf 4(2)} (2007), 15-29.
34
ORIGINAL_ARTICLE
AN EXTENDED FUZZY ARTIFICIAL NEURAL NETWORKS
MODEL FOR TIME SERIES FORECASTING
Improving time series forecastingaccuracy is an important yet often difficult task.Both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. In this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series forecasting, usingautoregressive integrated moving average models. In the proposedmodel, by first modeling the linear components, autoregressive integrated moving average models arecombined with the these hybrid models to yield amore general and accurate forecasting model than thetraditional hybrid artificial neural networks and fuzzy models. Empirical results for financialtime series forecasting indicate that the proposed model exhibitseffectively improved forecasting accuracy and hence is an appropriate forecasting tool for financial timeseries forecasting.
http://ijfs.usb.ac.ir/article_286_8f60e4aa7f4205bae77bc7d15817e122.pdf
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66
10.22111/ijfs.2011.286
Auto-regressive integrated moving average (ARIMA)
Artificial neural networks (ANNs)
Fuzzy regression
Fuzzy logic
Time series forecasting
Financial markets
Mehdi
Khashe
khashei@in.iut.ac.ir
true
1
Industrial Engineering Department, Isfahan University of Technol-
ogy, Isfahan, Iran
Industrial Engineering Department, Isfahan University of Technol-
ogy, Isfahan, Iran
Industrial Engineering Department, Isfahan University of Technol-
ogy, Isfahan, Iran
LEAD_AUTHOR
Mehdi
Bijari
bijari@cc.iut.ac.ir
true
2
Industrial Engineering Department, Isfahan University of Technology,
Isfahan, Iran
Industrial Engineering Department, Isfahan University of Technology,
Isfahan, Iran
Industrial Engineering Department, Isfahan University of Technology,
Isfahan, Iran
AUTHOR
Seyed Reza
Hejazi
rehejazi@cc.iut.ac.ir
true
3
Industrial Engineering Department, Isfahan University of Tech-
nology, Isfahan, Iran
Industrial Engineering Department, Isfahan University of Tech-
nology, Isfahan, Iran
Industrial Engineering Department, Isfahan University of Tech-
nology, Isfahan, Iran
AUTHOR
bibitem{1} A. R. Arabpour and M. Tata, {it Estimating the parameters of a
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fuzzy linear regression model}, Iranian Journal of Fuzzy Systems,
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{bf5} (2008), 1-19.
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hybrid genetic-neural architecture for stock indexes forecasting},
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H. van Dijk, {it Combined forecasts from linear and nonlinear
134
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(2002), 421-438.
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138
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173
{bf35} (2008), 525-529.
174
ORIGINAL_ARTICLE
T-S FUZZY MODEL-BASED MEMORY CONTROL FOR
DISCRETE-TIME SYSTEM WITH RANDOM INPUT DELAY
A memory control for T-S fuzzy discrete-time systems with sto- chastic input delay is proposed in this paper. Dierent from the common assumptions on the time delay in the existing literatures, it is assumed in this paper that the delays vary randomly and satisfy some probabilistic dis- tribution. A new state space model of the discrete-time T-S fuzzy system is derived by introducing some stochastic variables satisfying Bernoulli random binary distribution and using state augmentation method, some criterion for the stochastic stability analysis and stabilization controller design are derived for T-S fuzzy systems with stochastic time-varying input delay. Finally, a nu- merical example is given to demonstrate the eectiveness and the merit of the proposed method.
http://ijfs.usb.ac.ir/article_287_efae2eb122d0757d907537e26d6bc7e6.pdf
2011-10-18T11:23:20
2018-09-18T11:23:20
67
79
10.22111/ijfs.2011.287
Memory control
Fuzzy system
Random input delay
Discrete-time
system
Jinliang
Liu
liujinliang@vip.163.com
true
1
Department of Applied Mathematics, Nanjing University of Finance
and Economics, Nanjing, 210046, P. R. China and the College of Information Sciences,
Donghua University, Shanghai, 201620, P. R. China
Department of Applied Mathematics, Nanjing University of Finance
and Economics, Nanjing, 210046, P. R. China and the College of Information Sciences,
Donghua University, Shanghai, 201620, P. R. China
Department of Applied Mathematics, Nanjing University of Finance
and Economics, Nanjing, 210046, P. R. China and the College of Information Sciences,
Donghua University, Shanghai, 201620, P. R. China
LEAD_AUTHOR
Zhou
Gu
guzhouok@yahoo.com.cn
true
2
Power Engineering, Nanjing Normal University, Nanjing, Jiangsu, 210042,
P. R. China
Power Engineering, Nanjing Normal University, Nanjing, Jiangsu, 210042,
P. R. China
Power Engineering, Nanjing Normal University, Nanjing, Jiangsu, 210042,
P. R. China
AUTHOR
Hua
Han
2070967@mail.dhu.edu.cn
true
3
College of Information Science and Technology, Donghua University,
Shanghai, 201620, P. R.China
College of Information Science and Technology, Donghua University,
Shanghai, 201620, P. R.China
College of Information Science and Technology, Donghua University,
Shanghai, 201620, P. R.China
AUTHOR
Songlin
Hu
songlin621@126.com
true
4
Department of Control Science and Engineering, Huazhong University
of Science and Technology, Wuhan, Hubei, 430074, P. R. China
Department of Control Science and Engineering, Huazhong University
of Science and Technology, Wuhan, Hubei, 430074, P. R. China
Department of Control Science and Engineering, Huazhong University
of Science and Technology, Wuhan, Hubei, 430074, P. R. China
AUTHOR
[1] Y. Cao and P. Frank, Analysis and synthesis of nonlinear time-delay systems via fuzzy control
1
approach, IEEE Trans. On fuzzy systems, 8(2) (2000), 200-211.
2
[2] Y. Cao and P. Frank, Stability analysis and synthesis of nonlinear time-delay systems via
3
linear Takagi{Sugeno fuzzy models, Fuzzy Sets and Systems, 142(2) (2001), 213-229.
4
[3] B. Chen, X. Liu, S. Tong and C. Lin, Observer-based stabilization of TS fuzzy systems with
5
input delay, IEEE Trans. On Fuzzy Systems, 16(3) (2008), 652-663.
6
[4] B. Chen, X. Liu, C. Lin and K. Liu, Robust H1 control of Takagi{Sugeno fuzzy systems with
7
state and input time delays, Fuzzy Sets and Systems, 160(4) (2009), 403-422.
8
[5] G. Feng and X. Guan, Delay-dependent stability analysis and controller synthesis for discrete-
9
time T-S fuzzy systems with time delays, IEEE Trans. On Fuzzy Systems, 13(5) (2005),
10
[6] G. Feng, A survey on analysis and design of model-based fuzzy control systems, IEEE Trans.
11
On Fuzzy systems, 14(5) (2006), 676-697.
12
[7] E. Fridman and U. Shaked, Delay-dependent H1 control of uncertain discrete delay systems,
13
European Journal of Control, 11(1) (2005), 29-37.
14
[8] E. Fridman and U. Shaked, Stability and guaranteed cost control of uncertain discrete delay
15
systems, International Journal of Control, 78(4) (2005), 235-246.
16
[9] H. Gao and T. Chen, New results on stability of discrete-time systems with time-varying
17
state delay, IEEE Trans. on Automatic Control, 52(2) (2007), 328-334.
18
[10] H. Gao, X. Meng and T. Chen, Stabilization of networked control systems with a new delay
19
characterization, IEEE Trans. On Automatic Control, 53(9) (2008), 2142-2148.
20
[11] X. Guan and C. Chen, Delay-dependent guaranteed cost control for TS fuzzy systems with
21
time delays, IEEE Trans. On Fuzzy Systems, 12(2) (2004), 236-249.
22
[12] H. Huang and F. Shi, Robust H1 control for TCS time-varying delay systems with norm
23
bounded uncertainty based on LMI approach, Iranian Journal of Fuzzy Systems, 6(1) (2009),
24
[13] J. Liu, W. Yu, Z. Gu and S. Hu, H1 ltering for time-delay systems with markovian jumping
25
parameters: delay partitioning approach, Journal of the Chinese Institute of Engineers, 33(3)
26
(2010), 357-365.
27
[14] J. Liu, Z. Gu, E. Tian and R. Yan, New results on H1 lter design for nonlinear systems with
28
time-delay through a T-S fuzzy model approach, International Journal of Systems Science,
29
First published on: 16 August 2010 (iFirst).
30
[15] X. Liu and Q. Zhang, Approaches to quadratic stability conditions and H1 control designs
31
for TS fuzzy systems, IEEE Trans. On Fuzzy Systems, 11(6) (2003), 830-839.
32
[16] K. Lee, J. Kim, E. Jeung and H. Park, Output feedback robust H1 control of uncertainfuzzy
33
dynamic systems with time-varying delay, IEEE Trans. On Fuzzy systems, 8(6) (2000), 657-
34
[17] C. Peng, Y. Tian and E. Tian, Improved delay-dependent robust stabilization conditions of
35
uncertain T{S fuzzy systems with time-varying delay, Fuzzy Sets and Systems, 159(20)
36
(2008), 2713-2729.
37
[18] C. Peng, D. Yue and Y. Tian, New approach on robust delay-dependent H1 control for
38
uncertain TS fuzzy systems with interval time-varying delay, IEEE Trans. On Fuzzy Systems,
39
17(4) (2009), 890-900.
40
[19] T. Takagi and M. Sugeno, Fuzzy identication of systems and its applications to modeling
41
and control, IEEE Transactions on Systems, Man, and Cybernetics, 15(1) (1985), 116-132.
42
[20] E. Tian and C. Peng, Delay-dependent stability analysis and synthesis of uncertain T{S fuzzy
43
systems with time-varying delay, Fuzzy Sets and Systems, 157(4) (2006), 544-559.
44
[21] H. Wang, K. Tanaka and M. Grin, An approach to fuzzy control of nonlinear systems:
45
stability and design issues, IEEE Trans. On Fuzzy Systems, 4(1) (1996), 14-23.
46
[22] J. Yoneyama, Robust stability and stabilization for uncertain Takagi{Sugeno fuzzy time-delay
47
systems, Fuzzy Sets and Systems, 158(2) (2007), 115-134.
48
[23] D. Yue, Q. Han and J. Lam, Network-based robust H1 control of systems with uncertainty,
49
Automatica, 41(6) (2005), 999-1007.
50
[24] D. Yue, Q. Han and C. Peng, State feedback controller design of networked control systems,
51
IEEE Trans. On Circuits and Systems Part II: Express Briefs, 51(11) (2004), 640-644.
52
ORIGINAL_ARTICLE
EXPECTED PAYOFF OF TRADING STRATEGIES INVOLVING
EUROPEAN OPTIONS FOR FUZZY FINANCIAL MARKET
Uncertainty inherent in the financial market was usually consid- ered to be random. However, randomness is only one special type of uncer- tainty and appropriate when describing objective information. For describing subjective information it is preferred to assume that uncertainty is fuzzy. This paper defines the expected payoof trading strategies in a fuzzy financial market within the framework of credibility theory. In addition, a computable integral form is obtained for expected payoof each strategy.
http://ijfs.usb.ac.ir/article_288_bb59ddc4aacf0649fad5e0dd252ffed3.pdf
2011-10-18T11:23:20
2018-09-18T11:23:20
81
94
10.22111/ijfs.2011.288
Credibility measure
Liu process
Expected value
Fuzzy process
Zhongfeng
Qin
qin@buaa.edu.cn, qzf05@mails.tsinghua.edu.cn
true
1
School of Economics and Management, Beihang University, Beijing
100191, China
School of Economics and Management, Beihang University, Beijing
100191, China
School of Economics and Management, Beihang University, Beijing
100191, China
LEAD_AUTHOR
Xiang
Li
xiang-li04@mail.tsinghua.edu.cn
true
2
The State Key Laboratory of Rail Traffic Control and Safety, Beijing
Jiaotong University, Beijing 100044, China
The State Key Laboratory of Rail Traffic Control and Safety, Beijing
Jiaotong University, Beijing 100044, China
The State Key Laboratory of Rail Traffic Control and Safety, Beijing
Jiaotong University, Beijing 100044, China
AUTHOR
[1] M. Baxter and A. Rennie, Financial calculus: an introduction to derivatives pricing, Cam-
1
bridge University Press, 1996.
2
[2] F. Black and M. Scholes, The pricing of option and corporate liabilities, J. Polit. Econ., 81
3
(1973), 637-654.
4
[3] K. A. Chrysas and B. K. Papadopoulos, On theoretical pricing of options with fuzzy esti-
5
mators, J. Comput. Appl. Math., 223 (2009), 552-566.
6
[4] J. Hull, Options, futures and other derivative securities, 5th ed., Prentice-Hall, 2006.
7
[5] Z. Landsman, Minimization of the root of a quadratic functional under a system of ane
8
equality constraints with application to portfolio management, J. Comput. Appl. Math., 220
9
(2008), 739-748.
10
[6] C. Lee, G. Tzeng and S. Wang, A new application of fuzzy set theory to the Black-Scholes
11
option pricing model, Expert Syst. Appl., 29 (2005), 330-342.
12
[7] X. Li and B. Liu, A sucient and necessary condition for credibility measures, Int. J. Un-
13
certain. Fuzz., 14 (2006), 527-535.
14
[8] X. Li and B. Liu, Maximum entropy principle for fuzzy variables, Int. J. Uncertain. Fuzz.,
15
15 (2007), 43-52.
16
[9] X. Li and Z. Qin, Expected value and variance of geometric Liu process, Far East Journal of
17
Experimental and Theoretical Artical Intelligence, 1(2) (2008), 127-135.
18
[10] B. Liu, Uncertainty theory, lst ed., Springer-Verlag, Berlin, 2004.
19
[11] B. Liu, Uncertainty theory, 2nd ed., Springer-Verlag, Berlin, 2007.
20
[12] B. Liu, Uncertainty theory, 3nd ed., http://orsc.edu.cn/liu/ut.pdf.
21
[13] B. Liu, Fuzzy process, hybrid process and uncertain process, J. Uncertain Syst., 2 (2008),
22
[14] B. Liu and Y. K. Liu, Expected value of fuzzy variable and fuzzy expected value models, IEEE
23
T. Fuzzy Syst., 10 (2002), 445-450.
24
[15] Y. K. Liu and J. Gao, The independence of fuzzy variables in credibility theory and its
25
applications, Int. J. Uncertain. Fuzz., 15(Supp.2) (2007), 1-20.
26
[16] R. Merton, Theory of rational option pricing, Bell J. Econ. Manage. Sci., 4 (1973), 141-183.
27
[17] S. Muzzioli and C. Torricelli, A multiperiod binomial model for pricing options in a vague
28
world, J. Econ. Dynam. Control, 28 (2004), 861-887.
29
[18] Z. Qin and X. Li, Option pricing formula for fuzzy nancial market, J. Uncertain Syst., 2
30
(2008), 17-21.
31
[19] Z. Qin, X. Li and X. Ji, Portfolio selection based on fuzzy cross-entropy, J. Comput. Appl.
32
Math., 228 (2009), 139-149.
33
[20] Z. Qin and X. Li, Fuzzy calculus for nance, http://orsc.edu.cn/process/fc.pdf.
34
[21] P. A. Samuelson, Rational theory of warrant prices, Ind. Manage. Rev., 6 (1965), 13-31.
35
[22] K. Thiagarajah, S. S. Appadoo and A. Thavaneswaran, Option valuation model with adaptive
36
fuzzy numbers, Comput. Math. Appl., 53 (2007), 831-841.
37
[23] E. Vercher, Portfolios with fuzzy returns: Selection strategies based on semi-innite program-
38
ming, J. Comput. Appl. Math., 217 (2008), 381-393.
39
[24] Y. Wang, Mining stock price using fuzzy rough set system, Expert Syst. Appl., 24 (2003),
40
[25] H. Wu, Pricing European options based on the fuzzy pattern of Black-Scholes formula, Com-
41
put. Oper. Res., 31 (2004), 1069-1081.
42
[26] H. Wu, Using fuzzy sets theory and Black-Scholes formula to generate pricing boundaries of
43
European options, Appl. Math. Comput., 185 (2007), 136-146.
44
[27] Y. Yoshida, The valuation of European options in uncertain environment, Eur. J. Oper. Res.,
45
145 (2003), 221-229.
46
[28] L. A. Zadeh, Fuzzy sets, Information and Control, 8 (1965), 338-353.
47
ORIGINAL_ARTICLE
AGILITY EVALUATION IN PUBLIC SECTOR USING FUZZY
LOGIC
Agility metrics are difficult to define in general, mainly due to the multidimensionality and vagueness of the concept of agility itself. In this paper, a knowledge-based framework is proposed for the measurement and assessment of public sector agility using the A.T.Kearney model. Fuzzy logic provides a useful tool for dealing with decisions in which the phenomena are imprecise and vague. In the paper, we use the absolute agility index together with fuzzy logic to address the ambiguity in agility evaluation in public sector in a case study.
http://ijfs.usb.ac.ir/article_289_827b59516d05499a98dc48a20937df47.pdf
2011-10-18T11:23:20
2018-09-18T11:23:20
95
111
10.22111/ijfs.2011.289
Agility index
Agility measuring
Fuzzy logic
Agile government
Public
sector
Nazar
Dahmardeh
nazar@hamoon.usb.ac.ir
true
1
Department of Economics, University of Sistan and Baluchestan,
Zahedan, Iran
Department of Economics, University of Sistan and Baluchestan,
Zahedan, Iran
Department of Economics, University of Sistan and Baluchestan,
Zahedan, Iran
AUTHOR
vahid
Pourshahabi
pourshahabi.vahid@gmail.com
true
2
Member of Young Researchers Club, Islamic Azad University,
Zahedan, Iran
Member of Young Researchers Club, Islamic Azad University,
Zahedan, Iran
Member of Young Researchers Club, Islamic Azad University,
Zahedan, Iran
LEAD_AUTHOR
[1] B. M. Arteta and R. E. Giachetti, A measure of agility as the complexity of the enterprise
1
system, Robotics and Computer-integrated Manufacturing, 20 (2004), 495-503.
2
[2] A. T. Kearney, Improving Performance in the Public Sector, 2003.
3
[3] E. Bottani, A fuzzy QFD approach to achieve agility, International Journal of Production
4
Economics, 2009.
5
[4] S. J. Chen and C. L. Hwang, Fuzzy multiple attribute decision making methods and application,
6
Springer, Berlin, Heidelberg, 1992.
7
[5] H. Hassanpour, H. R. Maleki and M. A. Yaghooni, A note on evaluation of fuzzy linear
8
regression models by comparing membership functions, Iranian Journal of Fuzzy Systems, 6
9
(2) (2009), 1-6.
10
[6] R. Hefner and N. Grumman, Six sigma applied throughout the lifecycle of an automated
11
decision system, (2006), 5-88.
12
[7] M. Jackson and C. Johansson, An agility analysis from a production system perspective,
13
Integrated Manufacturing Systems, 14(6) (2002), 482-488.
14
[8] C. T. Lin, H. Chiu and P. Y. Chu, Agility index in the supply chain, Int. J. Production
15
Economics, 100 (2006), 285-299.
16
[9] A. Maturo, On some structures of fuzzy numbers, Iranian Journal of Fuzzy Systems, 6(4)
17
(2009), 49-59.
18
[10] S. Parker and J. Barlett, Toward agile government, State service authority, 2008.
19
[11] A. W. Scheer, H. Kruppke, W. Jost and H. Kinderman, AGILITY, Aris Business process
20
management, 2007.
21
[12] B. Sherehiy, W. Karwowski and J. K. Layer, A review of enterprise agility: concepts, frameworks,
22
and attributes, International Journal of Industrial Ergonomics, 37 (2007), 445-460.
23
[13] P. M. Swafford, S. Ghosh and N. Murthy, The antecedents of supply chain agility of a firm:
24
scale development and model testing, Journal of Operations Management, 24 (2006), 170-
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[14] N. C. Tsourveloudis and K. P. Valavanis, On the measurement of enterprise agility, Journal
26
of Intelligent and Robotics Systems, 33 (2002), 329-342.
27
[15] M. Zain, R. C. Rose, I. Abdollah and M. Masrom, The relationship between information
28
technology acceptance and organizational agility in Malaysia, Information and Management,
29
42 (2005), 829-839.
30
ORIGINAL_ARTICLE
FUZZY GOULD INTEGRABILITY ON ATOMS
In this paper we study the relationships existing between total measurability in variation and Gould type fuzzy integrability (introduced and studied in [21]), giving a special interest on their behaviour on atoms and on finite unions of disjoint atoms. We also establish that any continuous real valued function defined on a compact metric space is totally measurable in the variation of a regular finitely purely atomic multisubmeasure and it is also Gould integrable with respect to regular finitely purely atomic multisubmeasures.
http://ijfs.usb.ac.ir/article_290_b34c259b2551ddd12d42404e31bf5bb9.pdf
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10.22111/ijfs.2011.290
Fuzzy Gould integral
Totally measurability (in variation)
(Multi)
(sub) measure
Atom
Finitely purely atomic
Regularity
Alina
Cristiana Gavrilut
gavrilut@uaic.ro
true
1
Faculty of Mathematics, Al. I. Cuza University, Iasi,
Romania
Faculty of Mathematics, Al. I. Cuza University, Iasi,
Romania
Faculty of Mathematics, Al. I. Cuza University, Iasi,
Romania
LEAD_AUTHOR
[1] M. Abbas, M. Imdad and D. Gopal, "-weak contractions in fuzzy metric spaces, Iranian
1
Journal of Fuzzy Systems, in print.
2
[2] M. Alimohammady, E. Ekici, S. Jafari and M. Roohi, On fuzzy upper and lower contra-
3
continuous multifunctions, Iranian Journal of Fuzzy Systems, in print.
4
[3] I. Altun, Some xed point theorems for single and multivalued mappings on ordered non-
5
archimedean fuzzy metric spaces, Iranian Journal of Fuzzy Systems, 7(1) (2010), 91-96.
6
[4] I. Chitescu, Finitely purely atomic measures and Lp-spaces, An. Univ. Bucuresti St. Natur.,
7
24 (1975), 23-29.
8
[5] I. Chitescu, Finitely purely atomic measures: coincidence and rigidity properties, Rend. Circ.
9
Mat. Palermo(2), 50(3) (2001), 455-476.
10
[6] I. Dobrakov, On submeasures, I, Dissertationes Math., 112 (1974), 5-35.
11
[7] L. Drewnowski, Topological rings of sets, continuous set functions, Integration, I, II, III,
12
Bull. Acad. Polon. Sci. Ser. Math. Astron. Phys., 20 (1972), 269-286.
13
[8] A. Gavrilut, A Gould type integral with respect to a multisubmeasure, Math. Slovaca, 58(1)
14
(2008), 1-20.
15
[9] A. Gavrilut, On some properties of the Gould type integral with respect to a multisubmeasure,
16
An. St. Univ. Iasi, 52(1) (2006), 177-194.
17
[10] A. Gavrilut, Non-atomicity and the Darboux property for fuzzy and non-fuzzy Borel/Baire
18
multivalued set functions, Fuzzy Sets and Systems, 160 (2009), 1308-1317.
19
[11] A. Gavrilut, Regularity and autocontinuity of set multifunctions, Fuzzy Sets and Systems,
20
160 (2009), 681-693.
21
[12] A. Gavrilut, A Lusin type theorem for regular monotone uniformly autocontinuous set mul-
22
tifunctions, Fuzzy Sets and Systems, 161 (2010), 2909-2918.
23
[13] A. Gavrilut and A. Croitoru, Non-atomicity for fuzzy and non-fuzzy multivalued set functions,
24
Fuzzy Sets and Systems, 160 (2009), 2106-2116.
25
[14] A. Gavrilut and A. Petcu, A Gould type integral with respect to a submeasure, An. St. Univ.
26
Iasi, Tomul LIII, 2 (2007), 351-368.
27
[15] A. Gavrilut and A. Petcu, Some properties of the Gould type integral with respect to a
28
submeasure, Bul. Inst. Pol. Iasi, LIII (LVII), 5 (2007), 121-131.
29
[16] G. G. Gould, On integration of vector-valued measures, Proc. London Math. Soc., 15 (1965),
30
[17] S. Hu and N. S. Papageorgiou, Handbook of multivalued analysis, Kluwer Acad. Publ., Dor-
31
drecht, I (1997).
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[18] E. Pap, Null-additive set functions, Kluwer Academic Publishers, Dordrecht-Boston-London,
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[19] Q. Jiang and H. Suzuki, Fuzzy measures on metric spaces, Fuzzy Sets and Systems, 83 (1996),
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[20] A. Precupanu and A. Croitoru, A Gould type integral with respect to a multimeasure, I, An.
35
St. Univ. Iasi, 48 (2002), 165-200.
36
[21] A. Precupanu, A. Gavrilut and A. Croitoru, A fuzzy Gould type integral, Fuzzy Sets and
37
Systems, 161 (2010), 661-680.
38
[22] H. Suzuki, Atoms of fuzzy measures and fuzzy integrals, Fuzzy Sets and Systems, 41 (1991),
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[23] C. Wu and S. Bo, Pseudo-atoms of fuzzy and non-fuzzy measures, Fuzzy Sets and Systems,
40
158 (2007), 1258-1272.
41
ORIGINAL_ARTICLE
ON GENERAL FUZZY RECOGNIZERS
In this paper, we de ne the concepts of general fuzzy recognizer, language recognized by a general fuzzy recognizer, the accessible and the coac- cessible parts of a general fuzzy recognizer and the reversal of a general fuzzy recognizer. Then we obtain the relationships between them and construct a topology and some hypergroups on a general fuzzy recognizer.
http://ijfs.usb.ac.ir/article_291_7143a73b4b2bcbd29a9bb6ed63d06f47.pdf
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(General) Fuzzy automata
General fuzzy recognizer
Accessibility
Coaccessibility
Topology
Hypergroup
M.
Horry
mohhorry@chamran-edu.ir
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
M. M.
Zahedi
zahedi mm@mail.uk.ac.ir
true
2
Department of Mathematics, Shahid Bahonar University of Kerman,
Kerman, Iran
Department of Mathematics, Shahid Bahonar University of Kerman,
Kerman, Iran
Department of Mathematics, Shahid Bahonar University of Kerman,
Kerman, Iran
AUTHOR
[1] S. Bozapalidis and O. L. Bozapalidoy, On the recognizability of fuzzy languages I, Fuzzy Sets
1
and Systems, 157 (2006), 2394-2402.
2
[2] S. Bozapalidis and O. L. Bozapalidoy, On the recognizability of fuzzy languages II, Fuzzy Sets
3
and Systems, 159(1) (2008), 107-113.
4
[3] S. Bozapalidis and O. L. Bozapalidoy, Fuzzy tree language recognizability, Fuzzy Sets and
5
Systems, 161(5) (2010), 716-734.
6
[4] P. Corsini, Prolegomena of hypergroup theory, Aviani Editore, 1993.
7
[5] P. Corsini and V. Leoreanu, Applications of hyperstructure theory, Kluwer Academic Pub-
8
lishers, Advances in Mathematics, 2003.
9
[6] M. Doostfatemeh and S. C. Kremer, New directions in fuzzy automata, International Journal
10
of Approximate Reasoning, 38 (2005), 175-214.
11
[7] M. Horry and M. M. Zahedi, Hypergroups and general fuzzy automata, Iranian Journal of
12
Fuzzy Systems, 6(2) (2009), 61-74.
13
[8] K. Kuratowski, Topology, Academic Presss, 1966.
14
[9] H. V. Kumbhojkar and S. R. Chaudhari, Fuzzy recognizers and recognizable sets, Fuzzy Sets
15
and Systems, 131(3) (2002), 381-392.
16
[10] J. N. Mordeson and D. S. Malik, Fuzzy automata and languages, theory and applications,
17
Chapman and Hall/CRC, London/Boca Raton, FL, 2002.
18
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Fuzzy Sets and Systems, 68 (1994), 83-92.
20
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Systems Sci., 3 (1969), 409-422.
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tically encoded into recurrent neural networks, IEEE Trans. Fuzzy Syst., 5(1) (1998), 76-89.
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27
tomata, Information Sciences, 8 (1975), 39-53.
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to pattern classif ication, Ph.D. dissertation Purdue University, IN, 1967.
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[18] M. M. Zahedi, M. Horry and K. Abolpor, Bifuzzy (General) topology on max-min general
33
fuzzy automata, Advanced in Fuzzy Mathematics, 3(1) (2008), 51-68.
34
ORIGINAL_ARTICLE
FUZZY SOFT SET THEORY AND ITS APPLICATIONS
In this work, we define a fuzzy soft set theory and its related properties. We then define fuzzy soft aggregation operator that allows constructing more efficient decision making method. Finally, we give an example which shows that the method can be successfully applied to many problems that contain uncertainties.
http://ijfs.usb.ac.ir/article_292_22928400ec0d727700fd251a4f63fa07.pdf
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10.22111/ijfs.2011.292
Fuzzy sets
Soft sets
Fuzzy soft sets
Soft aggregation
Fuzzy soft
aggregation
Aggregate fuzzy set
Naim
Cagman
naim.cagman@gop.edu.tr
true
1
Department of Mathematics, Faculty of Arts and Sciences, Gazios-
manpasa University, 60150 Tokat, Turkey
Department of Mathematics, Faculty of Arts and Sciences, Gazios-
manpasa University, 60150 Tokat, Turkey
Department of Mathematics, Faculty of Arts and Sciences, Gazios-
manpasa University, 60150 Tokat, Turkey
LEAD_AUTHOR
Serdar
Enginoglu
serdar.enginoglu@gop.edu.tr
true
2
Department of Mathematics, Faculty of Arts and Sciences, Gazios-
manpasa University, 60150 Tokat, Turkey
Department of Mathematics, Faculty of Arts and Sciences, Gazios-
manpasa University, 60150 Tokat, Turkey
Department of Mathematics, Faculty of Arts and Sciences, Gazios-
manpasa University, 60150 Tokat, Turkey
AUTHOR
Filiz
Citak
filiz.citak@gop.edu.tr
true
3
Department of Mathematics, Faculty of Arts and Sciences, Gaziosman-
pasa University, 60150 Tokat, Turkey
Department of Mathematics, Faculty of Arts and Sciences, Gaziosman-
pasa University, 60150 Tokat, Turkey
Department of Mathematics, Faculty of Arts and Sciences, Gaziosman-
pasa University, 60150 Tokat, Turkey
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1
theory, Comput. Math. Appl., 57 (2009), 1547-1553.
2
[2] H. Aktas. and N. C.
3
agman, Soft sets and soft groups, Information Sciences, 177 (2007), 2726-
4
agman and S. Enginoglu, Soft set theory and uni-int decision making, Eur. J. Oper.
5
Res., 207 (2010), 848-855.
6
agman and S. Enginoglu, Soft matrix theory and its decision making, Comput. Math.
7
Appl., 59(10) (2010), 3308-3314.
8
agman, F. Ctak and S. Enginoglu, Fuzzy parameterized fuzzy soft set theory and its
9
applications, Turk. J. Fuzzy Syst., 1(1) (2010), 21-35.
10
[6] D. Chen, E. C. C. Tsang, D. S. Yeung and X. Wang, The parameterization reduction of soft
11
sets and its applications, Comput. Math. Appl., 49 (2005), 757-763.
12
[7] D. Dubois, and H. Prade, Fuzzy set and systems: theory and applications, Academic Press,
13
New York, 1980.
14
[8] F. Feng, Y. B. Jun and X. Zhao, Soft semirings, Comput. Math. Appl., 56(10) (2008),
15
2621-2628.
16
[9] Y. B. Jun, Soft BCK/BCI-algebras, Comput. Math. Appl., 56 (2008), 1408-1413.
17
[10] Y. B. Jun and C. H. Park, Applications of soft sets in ideal theory of BCK/BCI-algebras,
18
Information Sciences, 178 (2008), 2466-2475.
19
[11] Y. B. Jun, K. J. Lee and C. H. Park, Soft set theory applied to commutative ideals in BCK-
20
algebras, J. Appl. Math. and Informatics, 26 (2008), 707-720.
21
[12] Y. B. Jun and C. H. Park, Applications of soft sets in Hilbert algebras, Iranian Journal of
22
Fuzzy Systems, 6(2) (2009), 75-88.
23
[13] Y. B. Jun, H. S. Kim and J. Neggers, Pseudo d-algebras, Information Sciences, 179 (2009),
24
1751-1759.
25
[14] Y. B. Jun, K. J. Lee and C. H. Park, Soft set theory applied to ideals in d-algebras, Comput.
26
Math. Appl., 57 (2009), 367-378.
27
[15] Z. Kong, L. Gao, L. Wang and S. Li, The normal parameter reduction of soft sets and its
28
algorithm, Comput. Math. Appl., 56 (2008), 3029-3037.
29
[16] Z. Kong, L. Gao and L. Wang, Comment on A fuzzy soft set theoretic approach to decision
30
making problems", J. Comput. Appl. Math., 223 (2009), 540-542.
31
[17] D. V. Kovkov, V. M. Kolbanov and D. A. Molodtsov, Soft sets theory-based optimization, J.
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Comput. Sys. Sc. Int., 46(6) (2007), 872-880.
33
[18] P. K. Maji, R. Biswas and A. R. Roy, Fuzzy soft sets, J. Fuzzy Math., 9(3) (2001), 589-602.
34
[19] P. K. Maji, A. R. Roy and R. Biswas, An application of soft sets in a decision making
35
problem, Comput. Math. Appl., 44 (2002), 1077-1083.
36
[20] P. K. Maji, R. Biswas and A. R. Roy, Soft set theory, Comput. Math. Appl., 45 (2003),
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4(1) (2008), 1-12.
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Math. Assoc., 5(1) (2008), 35-42.
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Nechetkie Sistemi I Myakie Vychisleniya, 1(1) (2006), 8-39.
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theory based classication, Algorithm. Lecture Notes In Computer Science, 3851 (2006),
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Grzymala-Busse, D. Miao, A. Skowron, Y. Yao, eds., Rough Sets and Knowledge Technology,
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business competitive capacity based on soft set, Stat. Methods. Med. Res., (2003), 52-54.
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of soft sets, In: J. Chen ,eds., Proceedings of ICSSSM-05, 2 (2005), 1104-1106.
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Comput. Appl. Math., 228 (2009), 326-333.
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case, In: Bing-Yuan Cao ,eds., Fuzzy Information and Engineering: Proceedings of ICFIE-
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ORIGINAL_ARTICLE
ON FUZZY UPPER AND LOWER CONTRA-CONTINUOUS
MULTIFUNCTIONS
This paper is devoted to the concepts of fuzzy upper and fuzzy lower contra-continuous multifunctions and also some characterizations of them are considered.
http://ijfs.usb.ac.ir/article_293_e763f5996120648619462d585714b4b9.pdf
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158
10.22111/ijfs.2011.293
Fuzzy topological space
Fuzzy multifunctions
Fuzzy lower contracontinuous
multifunction
Fuzzy upper contra-continuous multifunction
mohsen
Alimohammady
amohsen@umz.ac.ir
true
1
Department of Mathematics, University of Mazandaran, Babolsar,
Iran
Department of Mathematics, University of Mazandaran, Babolsar,
Iran
Department of Mathematics, University of Mazandaran, Babolsar,
Iran
AUTHOR
E.
Ekici
eekici@comu.edu.tr
true
2
Department of Mathematics, Canakkale Onsekiz Mart University, Terzioglu
Campus, 17020 Canakkale, Turkey
Department of Mathematics, Canakkale Onsekiz Mart University, Terzioglu
Campus, 17020 Canakkale, Turkey
Department of Mathematics, Canakkale Onsekiz Mart University, Terzioglu
Campus, 17020 Canakkale, Turkey
LEAD_AUTHOR
S.
Jafari
jafari@stofanet.dk
true
3
College of Vestsjaelland South, Herrestraede 11, 4200 Slagelse, Denmark
College of Vestsjaelland South, Herrestraede 11, 4200 Slagelse, Denmark
College of Vestsjaelland South, Herrestraede 11, 4200 Slagelse, Denmark
AUTHOR
M.
Roohi
mehdi.roohi@gmail.com
true
4
Ghaemshahr branch Islamic Azad University, Ghaemshahr, Iran
Ghaemshahr branch Islamic Azad University, Ghaemshahr, Iran
Ghaemshahr branch Islamic Azad University, Ghaemshahr, Iran
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[1] J. Albrycht and M. Maltoka, On fuzzy multivalued function, Fuzzy Sets and Systems, 12
1
(1984), 61-69.
2
[2] M. Alimohammady and M. Roohi, On fuzzy ' -continuous multifunction, J. Appl. Math.
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Stoch. Anal., (2006), 1-7.
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[6] I. Beg, Fuzzy multivalued functions, Bull. Allahabad Math. Soc., 21 (2006), 41-104.
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Math., 6(1) (1998), 127-131.
10
[8] K. R. Bhutani, J. Mordeson and A. Rosenfeld, On degrees of end nodes and cut nodes in
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12
[9] J. M. Cadenas and J. L. Verdegay, A primer on fuzzy optimization models and methods,
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Iranian Journal of Fuzzy Systems, 3(1) (2006), 1-21.
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[10] M. Caldas, E. Ekici, S. Jafari and T. Noiri, On the class of contra -continuous functions,
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Annales Univ. Sci. Budapest Sec. Math., 49 (2006), 75-86.
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Math. Sci., 19(2) (1996), 303-310.
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[14] E. Ekici and E. E. Kerre, On fuzzy contra-continuities, Advances in Fuzzy Mathematics, 1(1)
21
(2006), 35-44.
22
[15] E. Ekici, S. Jafari and T. Noiri, On upper and lower contra-continuous multifunctions,
23
Analele Stii. Univ. Cuza Iasi Mat., LIV, 1 (2008), 75-85.
24
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ORIGINAL_ARTICLE
Persian-translation vol. 8, no. 3, october 2011
http://ijfs.usb.ac.ir/article_2871_59c5f318f5712ee069575b099e9c9c90.pdf
2011-10-19T11:23:20
2018-09-18T11:23:20
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10.22111/ijfs.2011.2871