University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
8
3
2011
10
18
Cover vol. 8, no. 3, october 2011
0
EN
10.22111/ijfs.2011.2870
http://ijfs.usb.ac.ir/article_2870.html
http://ijfs.usb.ac.ir/article_2870_00c38aa58385d4e27951d5fc93c237ec.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
8
3
2011
10
17
A SOLUTION TO AN ECONOMIC DISPATCH PROBLEM BY A
FUZZY ADAPTIVE GENETIC ALGORITHM
1
21
EN
H.
Nezamabadi-pour
Electrical Engineering Department, Shahid Bahonar University
of Kerman, Kerman, Iran
nezam@mail.uk.ac.ir
S.
Yazdani
Electrical Engineering Department, Shahid Bahonar University of Kerman,
Kerman, Iran
sajjad.yazdani@gmail.com
M. M.
Farsangi
Electrical Engineering Department, Shahid Bahonar University of
Kerman, Kerman, Iran
mmaghfoori@mail.uk.ac.ir
M.
Neyestani
Electrical Engineering Department, Shahid Bahonar University of
Kerman, Kerman, Iran
mehdi2594@yahoo.com
10.22111/ijfs.2011.283
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.
Economic dispatch,Genetic Algorithm,%Fuzzy adaptive genetic algorithm,Non-smooth cost functions
http://ijfs.usb.ac.ir/article_283.html
http://ijfs.usb.ac.ir/article_283_e8f1a7a622ced9b8eb173f4167349fec.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
8
3
2011
10
17
Fuzzy relations, Possibility theory, Measures of uncertainty, Mathematical modeling.
23
33
EN
Michael Gr.
Voskoglou
Graduate Technological Educational Institute (T.E.I.),
School of Technological Applications, 263 34 Patras, Greece
voskoglou@teipat.gr
10.22111/ijfs.2011.284
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.
Fuzzy relations,Possibility theory,Measures of uncertainty,Mathematical modeling
http://ijfs.usb.ac.ir/article_284.html
http://ijfs.usb.ac.ir/article_284_9bab58dbf42b5b3133cbad5825aa1e52.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
8
3
2011
10
17
Optimal Control with Fuzzy Chance Constraints
35
43
EN
Saeed
Ramezanzadeh
Department of Mathematics, Payame Noor University, Tehran,
Iran and Department of Mathematics, Faculty of Technology, Olum Entezami University,
Tehran, Iran
ramezanzadeh@phd.pnu.ac.ir
Aghileh
Heydari
Department of Mathematics, Payame Noor University, Mashhad,
Iran
a_heidari@pnu.ac.ir
10.22111/ijfs.2011.285
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.
Fuzzy random
variable,Chance-constrained programming,Possibility
level
http://ijfs.usb.ac.ir/article_285.html
http://ijfs.usb.ac.ir/article_285_4e09bdb20249d87c4d326aba52f5de42.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
8
3
2011
10
17
AN EXTENDED FUZZY ARTIFICIAL NEURAL NETWORKS
MODEL FOR TIME SERIES FORECASTING
45
66
EN
Mehdi
Khashe
Industrial Engineering Department, Isfahan University of Technol-
ogy, Isfahan, Iran
khashei@in.iut.ac.ir
Mehdi
Bijari
Industrial Engineering Department, Isfahan University of Technology,
Isfahan, Iran
bijari@cc.iut.ac.ir
Seyed Reza
Hejazi
Industrial Engineering Department, Isfahan University of Tech-
nology, Isfahan, Iran
rehejazi@cc.iut.ac.ir
10.22111/ijfs.2011.286
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.
Auto-regressive integrated moving average (ARIMA),Artificial neural networks (ANNs),Fuzzy regression,Fuzzy Logic,Time series forecasting,Financial markets
http://ijfs.usb.ac.ir/article_286.html
http://ijfs.usb.ac.ir/article_286_8f60e4aa7f4205bae77bc7d15817e122.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
8
3
2011
10
18
T-S FUZZY MODEL-BASED MEMORY CONTROL FOR
DISCRETE-TIME SYSTEM WITH RANDOM INPUT DELAY
67
79
EN
Jinliang
Liu
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
liujinliang@vip.163.com
Zhou
Gu
Power Engineering, Nanjing Normal University, Nanjing, Jiangsu, 210042,
P. R. China
guzhouok@yahoo.com.cn
Hua
Han
College of Information Science and Technology, Donghua University,
Shanghai, 201620, P. R.China
2070967@mail.dhu.edu.cn
Songlin
Hu
Department of Control Science and Engineering, Huazhong University
of Science and Technology, Wuhan, Hubei, 430074, P. R. China
songlin621@126.com
10.22111/ijfs.2011.287
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.
Memory control,Fuzzy System,Random input delay,Discrete-time
system
http://ijfs.usb.ac.ir/article_287.html
http://ijfs.usb.ac.ir/article_287_efae2eb122d0757d907537e26d6bc7e6.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
8
3
2011
10
18
EXPECTED PAYOFF OF TRADING STRATEGIES INVOLVING
EUROPEAN OPTIONS FOR FUZZY FINANCIAL MARKET
81
94
EN
Zhongfeng
Qin
School of Economics and Management, Beihang University, Beijing
100191, China
qin@buaa.edu.cn, qzf05@mails.tsinghua.edu.cn
Xiang
Li
The State Key Laboratory of Rail Traffic Control and Safety, Beijing
Jiaotong University, Beijing 100044, China
xiang-li04@mail.tsinghua.edu.cn
10.22111/ijfs.2011.288
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.
Credibility measure,Liu process,Expected value,Fuzzy process
http://ijfs.usb.ac.ir/article_288.html
http://ijfs.usb.ac.ir/article_288_bb59ddc4aacf0649fad5e0dd252ffed3.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
8
3
2011
10
18
AGILITY EVALUATION IN PUBLIC SECTOR USING FUZZY
LOGIC
95
111
EN
Nazar
Dahmardeh
Department of Economics, University of Sistan and Baluchestan,
Zahedan, Iran
nazar@hamoon.usb.ac.ir
vahid
Pourshahabi
Member of Young Researchers Club, Islamic Azad University,
Zahedan, Iran
pourshahabi.vahid@gmail.com
10.22111/ijfs.2011.289
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.
Agility index,Agility measuring,Fuzzy Logic,Agile government,Public
sector
http://ijfs.usb.ac.ir/article_289.html
http://ijfs.usb.ac.ir/article_289_827b59516d05499a98dc48a20937df47.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
8
3
2011
10
18
FUZZY GOULD INTEGRABILITY ON ATOMS
113
124
EN
Alina
Cristiana Gavrilut
Faculty of Mathematics, Al. I. Cuza University, Iasi,
Romania
gavrilut@uaic.ro
10.22111/ijfs.2011.290
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.
Fuzzy Gould integral,Totally measurability (in variation),(Multi)
(sub) measure,Atom,Finitely purely atomic,Regularity
http://ijfs.usb.ac.ir/article_290.html
http://ijfs.usb.ac.ir/article_290_b34c259b2551ddd12d42404e31bf5bb9.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
8
3
2011
10
18
ON GENERAL FUZZY RECOGNIZERS
125
135
EN
M.
Horry
Shahid Chamran University of Kerman, Kerman, Iran
mohhorry@chamran-edu.ir
M. M.
Zahedi
Department of Mathematics, Shahid Bahonar University of Kerman,
Kerman, Iran
zahedi mm@mail.uk.ac.ir
10.22111/ijfs.2011.291
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.
(General) Fuzzy automata,General fuzzy recognizer,Accessibility,Coaccessibility,Topology,Hypergroup
http://ijfs.usb.ac.ir/article_291.html
http://ijfs.usb.ac.ir/article_291_7143a73b4b2bcbd29a9bb6ed63d06f47.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
8
3
2011
10
18
FUZZY SOFT SET THEORY AND ITS APPLICATIONS
137
147
EN
Naim
Cagman
Department of Mathematics, Faculty of Arts and Sciences, Gazios-
manpasa University, 60150 Tokat, Turkey
naim.cagman@gop.edu.tr
Serdar
Enginoglu
Department of Mathematics, Faculty of Arts and Sciences, Gazios-
manpasa University, 60150 Tokat, Turkey
serdar.enginoglu@gop.edu.tr
Filiz
Citak
Department of Mathematics, Faculty of Arts and Sciences, Gaziosman-
pasa University, 60150 Tokat, Turkey
filiz.citak@gop.edu.tr
10.22111/ijfs.2011.292
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.
Fuzzy sets,Soft sets,Fuzzy soft sets,Soft aggregation,Fuzzy soft
aggregation,Aggregate fuzzy set
http://ijfs.usb.ac.ir/article_292.html
http://ijfs.usb.ac.ir/article_292_22928400ec0d727700fd251a4f63fa07.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
8
3
2011
10
18
ON FUZZY UPPER AND LOWER CONTRA-CONTINUOUS
MULTIFUNCTIONS
149
158
EN
mohsen
Alimohammady
Department of Mathematics, University of Mazandaran, Babolsar,
Iran
amohsen@umz.ac.ir
E.
Ekici
Department of Mathematics, Canakkale Onsekiz Mart University, Terzioglu
Campus, 17020 Canakkale, Turkey
eekici@comu.edu.tr
S.
Jafari
College of Vestsjaelland South, Herrestraede 11, 4200 Slagelse, Denmark
jafari@stofanet.dk
M.
Roohi
Ghaemshahr branch Islamic Azad University, Ghaemshahr, Iran
mehdi.roohi@gmail.com
10.22111/ijfs.2011.293
This paper is devoted to the concepts of fuzzy upper and fuzzy lower contra-continuous multifunctions and also some characterizations of them are considered.
Fuzzy topological space,Fuzzy multifunctions,Fuzzy lower contracontinuous
multifunction,Fuzzy upper contra-continuous multifunction
http://ijfs.usb.ac.ir/article_293.html
http://ijfs.usb.ac.ir/article_293_e763f5996120648619462d585714b4b9.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
8
3
2011
10
19
Persian-translation vol. 8, no. 3, october 2011
161
171
EN
10.22111/ijfs.2011.2871
http://ijfs.usb.ac.ir/article_2871.html
http://ijfs.usb.ac.ir/article_2871_59c5f318f5712ee069575b099e9c9c90.pdf