2013
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4
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SsT decomposition method for solving fully fuzzy linear systems
2
2
The SST decomposition method for solving system of linear equations make it possible to obtain the values of roots of the system with the specified accuracy as the limit of the sequence of some vectors. In this topic we are going to consider vectors as fuzzy vectors. We have considered a numerical example and tried to find out solution vector x in fuzzified form using method of SST decomposition.
1

275
280


K.
Jaikumar
Department of Mathematics, Dharmapuram Adhinam
Arts College, Dharmapuram, Mayiladuthurai, Tamilnadu,
India609001.
Department of Mathematics, Dharmapuram Adhinam
Art
Iran
kjkdaac@gmail.com


S.
Sunantha
Department of Mathematics, Vivekananda Arts and
Science College for Women, Thenpathi, NSB Nagar,
Sirkali, Tamilnadu, India609111.
Department of Mathematics, Vivekananda Arts
Iran
triangular fuzzy numbers
System of linear equations
SST Decomposition Method
Numerical solution of fuzzy differential equations under generalized
differentiability by fuzzy neural network
2
2
In this paper, we interpret a fuzzy differential equation by using the strongly generalized differentiability concept. Utilizing the Generalized characterization Theorem. Then a novel hybrid method based on learning algorithm of fuzzy neural network for the solution of differential equation with fuzzy initial value is presented. Here neural network is considered as a part of large eld called neural computing or soft computing. The model nds the approximated solution of fuzzy differential equation inside of its domain for the close enough neighborhood of the fuzzy initial point. We propose a learning algorithm from the cost function for adjusting of fuzzy weights.
1

281
297


M.
Mosleh
Department of Mathematics, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran.
Department of Mathematics, Firoozkooh Branch,
Iran
mosleh@iaufb:ac:ir
Fuzzy neural networks
Fuzzy dierential equations
Feedforward neural network
Learning algorithm
Utilizing a new feedback fuzzy neural network for solving a system of
fuzzy equations
2
2
This paper intends to offer a new iterative method based on articial neural networks for finding solution of a fuzzy equations system. Our proposed fuzzied neural network is a velayer feedback neural network that corresponding connection weights to output layer are fuzzy numbers. This architecture of articial neural networks, can get a real input vector and calculates its corresponding fuzzy output. In order to nd the approximate solution of the fuzzy system that supposedly has a real solution, rst a cost function is dened for the level sets of the fuzzy network and target output. Then a learning algorithm based on the gradient descent method is used to adjust the crisp input signals. The present method is illustrated by several examples with computer simulations.
1

299
307


A.
Jafarian
Department of Mathematics, Urmia Branch, Islamic
Azad University, Urmia, Iran.
Department of Mathematics, Urmia Branch,
Iran
jafarian5594@yahoo.com


S.
Measoomy Nia
Department of Mathematics, Urmia Branch, Islamic
Azad University, Urmia, Iran.
Department of Mathematics, Urmia Branch,
Iran
System of fuzzy equations
Fuzzy feedback neural network (FFNN)
Cost Function
Learning algorithm
Fully fuzzy linear programming with inequality constraints
2
2
Fuzzy linear programming problem occur in many elds such as mathematical modeling, Control theory and Management sciences, etc. In this paper we focus on a kind of Linear Programming with fuzzy numbers and variables namely Fully Fuzzy Linear Programming (FFLP) problem, in which the constraints are in inequality forms. Then a new method is proposed to ne the fuzzy solution for solving (FFLP). Numerical examples are providing to illustrate the method.
1

309
316


SH.
Nasseri
Department of Mathematics, University of Mazandaran, Babolsar ,Iran.
Department of Mathematics, University of
Iran
nasseri@umz.ac.ir


E.
Behmanesh
Department of Mathematics, University of Mazandaran, Babolsar ,Iran.
Department of Mathematics, University of
Iran


F.
Taleshian
Department of Mathematics, University of Mazandaran, Babolsar ,Iran.
Department of Mathematics, University of
Iran


M.
Abdolalipoor
Department of Mathematics, University of Tabriz,
Tabriz , Iran.
Department of Mathematics, University of
Iran


N. A.
TaghiNezhad
Department of Mathematics, University of Mazandaran, Babolsar ,Iran.
Department of Mathematics, University of
Iran
Fuzzy numbers
linear programming
Fuzzy linear programming
membership function
Ranking function
Singular constrained linear systems
2
2
In the linear system Ax = b the points x are sometimes constrained to lie in a given subspace S of column space of A. Drazin inverse for any singular or nonsingular matrix, exist and is unique. In this paper, the singular consistent or inconsistent constrained linear systems are introduced and the effect of Drazin inverse in solving such systems is investigated. Constrained linear system arise in electrical network theory.
1

317
323


M.
Nikuie
Young Researchers and Elite Club, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
Young Researchers and Elite Club, Tabriz
Iran
nikoie_m@yahoo.com


M. K.
Mirnia
Department of Computer engineering, Tabriz Branch,
Islamic Azad University, Tabriz, Iran.
Department of Computer engineering, Tabriz
Iran
Singular matrix
Drazin inverse
Constrained systems
BottDun inverse
Some traveling wave solutions of soliton family
2
2
Solitons are ubiquitous and exist in almost every area from sky to bottom. For solitons to appear, the relevant equation of motion must be nonlinear. In the present study, we deal with the KortewegdeVries (KdV), Modied Kortewegde Vries (mKdV) and Regularised LongWave (RLW) equations using Homotopy Perturbation method (HPM). The algorithm makes use of the HPM to determine the initial expansion coecients using the initial value and boundary conditions. The physical structures of the nonlinear dispersive equation have been investigated for different parameters involved. It is shown how the nature of the waves look like in a simple way by considering the value of a certain single combination of constant parameters. The proposed scheme is standard, direct and computerized, which allow us to do complicated and tedious algebraic calculations. The ease of using this method to determine shock or solitary type of solutions, shows its power.
1

325
339


S.
Dhawan
Department Of Mathematics, Dr. B. R. Ambedkar National Institute Of Technology Jalandhar, India.
Department Of Mathematics, Dr. B. R. Ambedkar
Iran
dhawan311@gmail.com


S.
Kumar
Department Of Mathematics, Dr. B. R. Ambedkar National Institute Of Technology Jalandhar, India.
Department Of Mathematics, Dr. B. R. Ambedkar
Iran
Nonlinear partial differential equations
solitary waves
Homotopy perturbation method (HPM)
Strong convergence for variational inequalities and equilibrium
problems and representations
2
2
We introduce an implicit method for nding a common element of the set of solutions of systems of equilibrium problems and the set of common xed points of a sequence of nonexpansive mappings and a representation of nonexpansive mappings. Then we prove the strong convergence of the proposed implicit schemes to the unique solution of a variational inequality, which is the optimality condition for a minimization problem and is also a common xed point for a sequence of nonexpansive mappings and a representation of nonexpansive mappings.
1

341
354


E.
Soori
Department of Mathematics, University of Isfahan, Isfahan, Iran.
Department of Mathematics, University of
Iran
sori.e@lu.ac.ir
Representation
Equilibrium problem
fixed point
Nonexpansive mapping
Variational inequality
Solving nonlinear LaneEmden type equations with unsupervised
combined artificial neural networks
2
2
In this paper we propose a method for solving some wellknown classes of LaneEmden type equations which are nonlinear ordinary differential equations on the semiinnite domain. The proposed approach is based on an Unsupervised Combined Articial Neural Networks (UCANN) method. Firstly, The trial solutions of the differential equations are written in the form of feedforward neural networks containing adjustable parameters (the weights and biases); results are then optimized with the combined neural network. The proposed method is tested on series of LaneEmden differential equations and the results are reported. Afterward, these results are compared with the solution of other methods demonstrating the eciency and applicability of the proposed method.
1

355
366


K.
Parand
Department of Computer Sciences, Shahid Beheshti
University, Tehran, Iran.
Department of Computer Sciences, Shahid Beheshti
U
Iran


Z.
Roozbahani
Department of Computer Sciences, Shahid Beheshti
University, Tehran, Iran.
Department of Computer Sciences, Shahid Beheshti
U
Iran
roozbahani2@gmail.com


F.
Bayat Babolghani
Department of Computer Sciences, Shahid Beheshti
University, Tehran, Iran.
Department of Computer Sciences, Shahid Beheshti
U
Iran
LaneEmden type equations
Nonlinear ODE
Semiinfinite domain
Astrophysics
Artificial neural network
Combined neural network
On convergence of homotopy analysis method to solve the
Schrodinger equation with a power law nonlinearity
2
2
In this paper, the homotopy analysis method (HAM) is considered to obtain the solution of the Schrodinger equation with a power law nonlinearity. For this purpose, a theorem is proved to show the convergence of the series solution obtained from the proposed method. Also, an example is solved to illustrate the eciency of the mentioned algorithm and the hcurve is plotted to determine the region of convergence.
1

367
374


M. A.
Fariborzi Araghi
Department of Mathematics, Islamic Azad University,
Central Tehran Branch, P. O. Box 13185.768, Tehran,
Iran.
Department of Mathematics, Islamic Azad University
Iran
m_fariborzi@iauctb.ac.ir


S.
Naghshband
Department of Mathematics, Islamic Azad University,
Central Tehran Branch, P. O. Box 13185.768, Tehran,
Iran.
Department of Mathematics, Islamic Azad University
Iran
Schrodinger equation
Power law nonlinearity
Homotopy analysis method (HAM)
Convergence
The effects of outside board on firm value in Tehran Stock Exchange
from the perspective of information transaction costs
2
2
The aim of this study is to investigate the effects of outside board on rm value in Tehran Stock Exchange (TSE) from the perspective of information transaction costs. To do so, a sample of 96 firms listed in TSE is selected to be studied during the period of 20032012. Tobin q ratio is used to measure rm's value and bidask spread for information transaction costs. In addition to these variables, four control variables are adapted namely rm's characteristic, age, size and duality. The results of the study show that there is not a signicant relationship between outside board and firm's value. Investigating the relationship between outside board and rm's value, the results indicate that only in food and nonmetal industries, there is a negative relationship between outside board and firm's performance. Therefore, it can be concluded that not in all of industries outside board affects rm's value. Further, results do not prove the effects of outside board on information transaction cost. In addition, the results do not support that information transaction cost aect rm's value. Finally, the results also suggest that independence and presence of outside board of director member does not a effect firm's value in rms with lower information transaction cost.
1

375
386


S.
Jabbarzadeh Kangarlouei
Department of Accounting, Science and Research
Branch, Islamic Azad University, West Azarbyjan, Iran.
Department of Accounting, Science and Research
Bra
Iran
jabbarzadeh.s@gmail.com


B.
Kavasi
Department of Accounting, Science and Research
Branch, Islamic Azad University, West Azarbyjan, Iran.
Department of Accounting, Science and Research
Bra
Iran


M.
Motavassel
Department of Accounting, Science and Research
Branch, Islamic Azad University, West Azarbyjan, Iran.
Department of Accounting, Science and Research
Bra
Iran
Outside Board
Firm Value
Information Transaction Costs and Tehran Stock Exchange
Groups performance ranking based on inefficiency sharing
2
2
In the real world there are groups which composed of independent units. The conventional data envelopment analysis(DEA) model treats groups as units, ignoring the operation of individual units within each group.The current paper, investigates parallel system network approach proposed by Kao and modifies it. As modied Kao' model is more eligible to recognize ecient groups, a new ranking method is proposed based on a model which calculates eciencies with additional constraint that made model share constant ineciency among groups.To show advantages, modies model is applied to eciency calculation of both articial and real groups and results is compared with conventional DEA model and parallel system network model as well.Finally it is shown by tow numerical and empirical examples that ecient groups recognized by modied model how can be ranked according to proposed ranking model.
1

387
395


M.
Momeni
Department of Industrial Management, University of
Tehran, Iran.
Department of Industrial Management, University
Iran


G. R.
Jahanshahloo
Faculty of Mathematical Science and Computer Engineering, University for Teacher Education, Iran.
Faculty of Mathematical Science and Computer
Iran


M.
Rostamy Malkhalifeh
Department of Mathematics, Science and Research
Branch, Islamic Azad University ,Iran.
Department of Mathematics, Science and Research
Br
Iran


S.
Razavi
Department of Industrial Management, University of
Tehran, Iran.
Department of Industrial Management, University
Iran


K.
Yakideh
Department of Industrial Management, University of
Tehran, Iran.
Department of Industrial Management, University
Iran
yakideh@ymail.com
Data envelopment analysis
Group Ranking
Network DEA
Parallel Systems Efficiency
Efficient Groups
Ranking NetworkStructured DecisionMaking Units and Its Application in Bank Branches
2
2
Data envelopment analysis (DEA) is a method used for measuring the efficiency of decisionmaking units. Unlike the standard models, which assume decisionmaking units to be a black box, network data envelopment analysis focuses on the internal structure of these units. Some researchers have developed a twostage method where all the inputs are entirely used in the first stage, producing outputs which are subsequently fed as inputs to the second stage. These indices are introduced as intermediate indices. Here, it is assumed that congruent decisionmaking units have a twostage serial structure. In this structure, the first stage and second stages act as the supplier and the consumer of resources respectively. Two ranking models based on the efficiency cloud and the common set of weights concepts were developed for ranking networkstructured decisionmaking units. In the practical example presented in this study, 25 bank branches were ranked using the twostage method.
1

397
402


M.
Shahriari
Faculty of Management, UAE branch, Islamic Azad University, Dubai, UAE.
Faculty of Management, UAE branch,
Iran
shahriari@iau.ae
Data envelopment analysis (DEA)
Twostage
Ranking