University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
9
1
2012
02
01
Cover vol. 9, no.1, February 2012
0
EN
10.22111/ijfs.2012.2816
http://ijfs.usb.ac.ir/article_2816.html
http://ijfs.usb.ac.ir/article_2816_9276302627a083223f091bfc721de91a.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
9
1
2012
02
10
APPLICATION OF TABU SEARCH FOR SOLVING THE
BI-OBJECTIVE WAREHOUSE PROBLEM IN
A FUZZY ENVIRONMENT
1
19
EN
Anila
Gupta
School of Mathematics and Computer Applications, Thapar Univer-
sity, Patiala-147004, India
anilasingal@gmail.com
Amit
Kumar
School of Mathematics and Computer Applications, Thapar University,
Patiala-147004, India
amit rs iitr@yahoo.com
Mahesh
Kumar Sharma
School of Mathematics and Computer Applications, Thapar
University, Patiala-147004, India
mksharma@thapar.edu
10.22111/ijfs.2012.221
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.
Trapezoidal fuzzy numbers,Bi-objective warehouse problem,Ecient
solution,Tabu search
http://ijfs.usb.ac.ir/article_221.html
http://ijfs.usb.ac.ir/article_221_2400536ae1bcebd857003631acf8ca86.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
9
1
2012
02
10
FUZZY GRAVITATIONAL SEARCH ALGORITHM AN
APPROACH FOR DATA MINING
21
37
EN
Seyed Hamid
Zahiri
Department of Electrical Engineering, Faculty of Engineering,
Birjand University, Birjand, Iran
hzahiri@@birjand.ac.ir
10.22111/ijfs.2012.223
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).
Gravitational search algorithm,Fuzzy controller,Data mining,Rule
based classifier
http://ijfs.usb.ac.ir/article_223.html
http://ijfs.usb.ac.ir/article_223_8aafe00e6254010a39a49144e87459eb.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
9
1
2012
02
10
A NEW APPROACH TO STABILITY ANALYSIS OF FUZZY
RELATIONAL MODEL OF DYNAMIC SYSTEMS
39
48
EN
Arya
Aghili Ashtiani
Department of Electrical Engineering, Amirkabir University
of Technology (AUT), P. O. Box 15914, Tehran, Iran
arya.aghili@aut.ac.ir
Sayyed Kamaloddin
Yadavar Nikravesh
Department of Electrical Engineering, Amirk-
abir University of Technology (AUT), P. O. Box 15914, Tehran, Iran
nikravsh@aut.ac.ir
10.22111/ijfs.2012.224
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.
Fuzzy relational dynamic system (FRDS),Fuzzy relational model
(FRM),Linguistic stability analysis,Fuzzy relational stability
http://ijfs.usb.ac.ir/article_224.html
http://ijfs.usb.ac.ir/article_224_c42486dad4820bcdd105fb3f70d965ff.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
9
1
2012
02
10
ON SOLUTION OF A CLASS OF FUZZY BVPs
49
60
EN
Omid
Solaymani Fard
School of Mathematics and Computer Science, Damghan Uni-
versity, Damghan, Iran
osfard@du.ac.ir, omidsfard@gmail.com
Amin
Esfahani
School of Mathematics and Computer Science, Damghan University,
Damghan, Iran
amin@impa.br, esfahani@du.ac.ir
Ali
Vahidian Kamyad
Department of Mathematics, Ferdowsi University of Mashhad,
Mashhad, Iran
avkamyad@math.um.ac.ir
10.22111/ijfs.2012.225
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.
Fuzzy numbers,Fuzzy dierential equations,Boundary value
problems
http://ijfs.usb.ac.ir/article_225.html
http://ijfs.usb.ac.ir/article_225_64b3d7d1d93425ef5d2a6e2adb0af34c.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
9
1
2012
02
10
SECURING INTERPRETABILITY OF FUZZY MODELS FOR
MODELING NONLINEAR MIMO SYSTEMS USING
A HYBRID OF EVOLUTIONARY ALGORITHMS
61
77
EN
Mojtaba
Eftekhari
Faculty of Islamic Azad University, Sirjan branch, ,Sirjan, Ker-
man, Iran
m.eftekhari59@gmail.com
Mahdi
Eftekhari
Department of Computer Engineering, School of Engineering,
Shahid Bahonar University of Kerman, Kerman, Iran
m.eftekhari@uk.ac.ir
Maryam
Majidi
Department of Computer Engineering, School of Engineering, Shahid
Bahonar University of Kerman, Kerman, Iran
majena67@yahoo.com
Hossein
Nezamabadi pour
Department of Electrical Engineering, School of Engi-
neering, Shahid Bahonar University of Kerman, Kerman, Iran
nezam h@yahoo.com
10.22111/ijfs.2012.226
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.
Multi-objective,Evolutionary,Fuzzy identication,Compact,Inter-
pretability
http://ijfs.usb.ac.ir/article_226.html
http://ijfs.usb.ac.ir/article_226_cc28b7c778fbedd749288307752c17c8.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
9
1
2012
02
11
ESTIMATORS BASED ON FUZZY RANDOM VARIABLES AND
THEIR MATHEMATICAL PROPERTIES
79
95
EN
M. G.
Akbari
Department of Statistics, Faculty of Sciences, University of Birjand,
Southern Khorasan, Birjand
mga13512@yahoo.com
M.
Khanjari Sadegh
Department of Statistics, Faculty of Sciences, University of
Birjand, Southern Khorasan, Birjand
g_z_akbari@yahoo.com
10.22111/ijfs.2012.227
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.
Fuzzy random variable,Fuzzy parameter,Signed distance,L2- metric,Fuzzy estimator,Fuzzy unbiased estimator,Fuzzy sufficient estimator,Fuzzy risk function
http://ijfs.usb.ac.ir/article_227.html
http://ijfs.usb.ac.ir/article_227_b17c576a5e5de627e505d9f09b0d933c.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
9
1
2012
02
11
A NEW METHOD TO REDUCE TORQUE RIPPLE IN
SWITCHED RELUCTANCE MOTOR USING
FUZZY SLIDING MODE
97
108
EN
S. R.
Mousavi-Aghdam
Faculty of electrical and computer engineering, University
of Tabriz, Tabriz, Iran
rmousavi@tabrizu.ac.ir
M. B. B.
Sharifian
Faculty of electrical and computer engineering, University of
Tabriz, Tabriz, Iran
sharifian@tabrizu.ac.ir
M. R.
Banaei
Department of electrical engineering, Faculty of engineering, azarbai-
jan, University of tarbiat moallem, Tabriz, Iran
m.banaei@azaruniv.edu
10.22111/ijfs.2012.228
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.
Fuzzy sliding control,Switched Reluctance Motor,Torque ripple re-
duction
http://ijfs.usb.ac.ir/article_228.html
http://ijfs.usb.ac.ir/article_228_eda29a4d8485fce43ad3b0b5e35789ed.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
9
1
2012
02
11
FUZZY SOFT MATRIX THEORY AND ITS APPLICATION IN
DECISION MAKING
109
119
EN
Naim
Cagman
Department of Mathematics, Faculty of Arts and Sciences, Gazios-
manpasa University, 60250 Tokat, Turkey
ncagman@gop.edu.tr
Serdar
Enginoglu
Department of Mathematics, Faculty of Arts and Sciences, Gazios-
manpasa University, 60250 Tokat, Turkey
serdarenginoglu@gop.edu.tr
10.22111/ijfs.2012.229
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.
Fuzzy soft sets,Fuzzy soft matrix,Products of fuzzy soft matrices,Fuzzy soft max-min decision making
http://ijfs.usb.ac.ir/article_229.html
http://ijfs.usb.ac.ir/article_229_b6d292816ccde2e91d91920714cb6245.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
9
1
2012
02
11
FUZZY LINEAR REGRESSION BASED ON
LEAST ABSOLUTES DEVIATIONS
121
140
EN
S. M.
Taheri
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
sm_taheri@yahoo.com
M.
Kelkinnama
Department of Mathematical Sciences, Isfahan University of Technology, Isfahan 84156-83111, Iran
m_ kelkinnama@yahoo.com
10.22111/ijfs.2012.230
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.
Fuzzy regression,Least absolutes deviations,Metric on fuzzy numbers,Similarity measure,Goodness of fit
http://ijfs.usb.ac.ir/article_230.html
http://ijfs.usb.ac.ir/article_230_d59e96e289e06159aecc01cdcd61a9dd.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
9
1
2012
02
11
TRANSPORT ROUTE PLANNING MODELS BASED
ON FUZZY APPROACH
141
158
EN
Julio
Brito
I. U. D. R., University of La Laguna, E-38271 Tenerife, Spain
jbrito@ull.es
Jose A.
Moreno
I. U. D. R., University of La Laguna, E-38271 Tenerife, Spain
jamoreno@ull.es
Jose L.
Verdegay
Department C. C. I. A., University of Granada, E-18071 Granada,
Spain
verdegay@decsai.ugr.es
10.22111/ijfs.2012.231
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.
Fuzzy optimization,Route planning,Soft computing
http://ijfs.usb.ac.ir/article_231.html
http://ijfs.usb.ac.ir/article_231_7ebf6519fbcb9484fdaa9a6e8d293100.pdf
University of Sistan and Baluchestan
Iranian Journal of Fuzzy Systems
1735-0654
9
1
2012
02
01
Persian-translation vol. 9, no.1, February 2012
161
170
EN
10.22111/ijfs.2012.2817
http://ijfs.usb.ac.ir/article_2817.html
http://ijfs.usb.ac.ir/article_2817_2daa4ebcebc8bddd67ee46bf11e33390.pdf