]>
2014
10
4
75
The Numerical Solution of Nonlinear Fredholm-hammerstein Integral Equations of the Second Kind Utilizing Chebyshev Wavelets
The Numerical Solution of Nonlinear Fredholm-hammerstein Integral Equations of the Second Kind Utilizing Chebyshev Wavelets
en
en
This paper describes a numerical scheme based on the Chebyshev wavelets constructed on the unit
interval and the Galerkin method for solving nonlinear Fredholm-Hammerstein integral equations of the
second kind. Chebyshev wavelets, as very well localized functions, are considerably effective to estimate
an unknown function. The integrals included in the method developed in the current paper are
approximated by the Gauss-Chebyshev quadrature rule. The proposed scheme reduces Fredholm-
Hammerstein integral equations to the solution of nonlinear systems of algebraic equations. The
properties of Chebyshev wavelets are used to make the wavelet coefficient matrix sparse which
eventually leads to the sparsity of the coefficients matrix of obtained system. Some illustrative examples
are presented to show the validity and efficiency of the new technique.
235
246
M. M.
Shamooshaky
P.
Assari
H.
Adibi
Fredholm-Hammerstein integral equation
Chebyshev wavelet
Galerkin method
Gauss- Chebyshev quadrature rule
sparse matrix
Article.1.pdf
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[1]
H. Kaneko, R. D. Noren, B. Novaprateep, Wavelet applications to the Petrov-Galerkin method for Hammersteinequations, Appl. Numer. Math. , 45 (2003), 255-273
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K. E. Atkinson, The Numerical Solution of Integral Equations of the Second Kind, Cambridge University Press, (1997)
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K. E. Atkinson, F. A. Potra, Projection and iterated projection methods for nonlinear integral equation, SIAM J. Numer. Anal. , 24 (1989), 1352-1373
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Y. Ordokhani, Solution of FredholmHammerstein integral equations with WalshHybrid functions, Int. Math. Forum. , 4 (2009), 969-976
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A. Alipanah, M. Dehghan, Numerical solution of the nonlinear Fredholm integral equations by positive definitefunctions, Appl. Math. Comput. , 190 (2007), 1754-1761
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J. Rashidinia, M. Zarebnia, New approach for numerical solution of Hammerstein integral equations, Appl. Math. Comput., 185 (2007), 147-154
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K. Maleknejad, K. Nedaiasl, Application of Sinc-collocation method for solving a class of nonlinear Fredholmintegral equations, Comput. Math. Appl. , 62 (2011), 3292-3303
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B. K. Alpert, A class of bases in L2 for the sparse representation of integral operators, SIAM J. Math. Anal. , 24 (1993), 246-262
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H. Adibi, P. Assari, Using CAS wavelets for numerical solution of Volterra integral equations of the second kind, Dyn. Contin.Discrete Impuls.Syst., Ser.A, Math.Anal. , 16 (2009), 673-685
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K. Maleknejad, K. Nouria, M. NosratiSahlan, Convergence of approximate solution of nonlinear Fredholm-Hammerstein integral equations, Commun. Nonlinear. Sci. Numer. Simulat. , 15 (2010), 1432-1443
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U. Lepik, E. Tamme, Solution of nonlinear Fredholm integral equations via the Haar wavelet method, Proc. Estonian Acad. Sci. Phys. Math., 56 (2007), 17-27
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H. Adibi, P. Assari, On the numerical solution of weakly singular Fredholm integral equations of the second kind using Legendre wavelets, J. Vib. Control. , 17 (2011), 689-698
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E. Babolian, F. Fattahzadeh, Numerical solution of differential equations by using Chebyshev wavelet operational matrix of integration, Appl. Math. Comput. , 188 (2007), 417-426
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E. Babolian, F. Fattahzadeh, Numerical computation method in solving integral equations by using Chebyshevwavelet operational matrix of integration, Appl. Math. Comput. , 188 (2007), 1016-1022
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H. Adibi, P. Assari, Chebyshev Wavelet Method for Numerical Solution of Fredholm Integral Equations of the First Kind , Math. Probl.Eng., 2010 (2010), 1-17
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L. Zhu, Q. Fan, Solving fractional nonlinear Fredholmintegro-differential equations by the second kind Chebyshev wavelet , Commun. Nonlinear. Sci. Numer. Simulat., 17 (2012), 2333-2341
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M. Ghasemi, M. AvassoliKajani, Numerical solution of time-varying delay systems by Chebyshev wavelets, Appl. Math. Model., 35 (2011), 5235-5244
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Y. Wang, Q. Fan, The second kind Chebyshev wavelet method for solving fractional differential equations, Appl. Math.Comput. , 218 (2012), 8592-8601
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Y. Li, Solving a nonlinear fractional differential equation using Chebyshev wavelets, Commun. Nonlinear. Sci. Numer. Simulat. , 11 (2011), 2284-2292
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I. Daubechies, Ten Lectures on Wavelets, SIAM/CBMS, Philadelphia PA (1992)
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Xu Dinghua, Numerical Solutions for Nonlinear Fredholm Integral Equations of the Second Kind and Their Superconvergence, J. Shanghai. Univ. , 1 (1997), 98-104
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]
Detour Distance and Self Centered Graphs
Detour Distance and Self Centered Graphs
en
en
This paper evaluates the detour distance of a graph and associated problems. We study about the detour eccentricity and average detour eccentricity of graphs and derive some of the properties that relates self centered graphs and detour distance. A characterization of tree is also discussed.
247
252
K. R. S.
Narayan
M. S.
Sunitha
Graphs
Detour distance in graphs
Centre of graphs
Detour eccentricity of graphs
Average detour eccentricity
Self centered graphs.
Article.2.pdf
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[1]
Chartrand, P. Zhang, Distance in graphs - taking the long view, AKCE Journal Of Graphs and Combinatorics , 1 (2004), 1-13
##[2]
P. Dankelmann, W. Goddard, C. S. Swart, The average eccentricity of a graph and its subgraphs, UtilitasMathematica , 65 (2004), 41-51
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F. Harary, Graph Theory, Addison Wesley, (1969)
##[4]
M. I. Huilgol, C. Ramaprakash, Cyclic edge extensions - self centered graphs, Journal of Mathematics and Computer Science , 10 (2014), 131-137
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L.-D. Tong, H.-T. Wang, Eccentric spectrum of a graph, Taiwanese Journal Of Mathematics , 12 (2008), 969-977
]
Measure Efficiency by Dea Model
Measure Efficiency by Dea Model
en
en
Parametric methods of efficiency measurement include the Stochastic Frontier Approach (SFA), Thick Frontier Approach (TFA) and Distribution Free Approach (DFA). These methods measure economic efficiency. Economic efficiency is a broader term than technical efficiency. It covers an optimal choice of the level and structure of inputs and outputs based on reactions to market prices. Basic efficiency is a ratio of output over input. To improve efficiency one has to either: (1) increase the outputs, (2) decrease the inputs, (3) if both outputs and inputs increase, the rate of increase for outputs should be greater than the rate of increase for inputs, or (4) if both outputs and inputs are decreasing, the rate of decrease for outputs should be lower than the rate of decrease for inputs.
253
257
Adel Asgari
Safdar
DEA (Data Envelopment Analysis)
Model
Efficiency
Article.3.pdf
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]
Ones Assignment Method for Solving Traveling Salesman Problem
Ones Assignment Method for Solving Traveling Salesman Problem
en
en
This paper presents an approach namely, ones assignment method, for solving the traveling salesman
problem. We have previously used this method for the assignment problem. We have slightly modified
the procedure to get a tour of the traveling salesman problem.
First we define the distance matrix, then by using determinant representation we obtain a reduced matrix
which has at least one 1 in each row and each column. Then by using the new method, we obtain an
optimal solution for traveling salesman problem by assigning ones to each row and each column. The new
method is based on creating some ones in the distance matrix and then try to find a complete solution to
their ones. At the end, this method is illustrated with some numerical examples.
258
265
Hadi
Basirzadeh
Assignment problem
Linear integer programming
traveling salesman problem.
Article.4.pdf
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[1]
Hadi Basirzadeh, Ones assignment method for solving assignment problems, Applied Mathematical Sciences, 6 (2012), 2345-2355
##[2]
Hadi Basirzadeh, Vahid Morovati, Aabbas Sayadi, A quick method to calculate the super-efficient point in multi-objective assignment problems, TJMCS, 10 (2014), 157-234
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Anshuman Sahu, Rudrajit Tapador, Solving the assignment problem using genetic algorithm and simulated annealing, IJAM, (2007)
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]
Negotiation Planning of Autonomous Agents in Multi-agent Environment
Negotiation Planning of Autonomous Agents in Multi-agent Environment
en
en
In negotiation process, when a conflict arises between people in a society, each will try to pose some arguments to resolve the conflict and convince the other party. Successful termination of many of these interactions is not achieved only via exchange of a series of single-message suggestions and requires that an arguer offer compelling justifications to prove his/her claim. Obviously, before negotiation is started, decision should be made about the terms and arguments that can convince the other side and terminate negotiations successfully. In other words, an exact plan of arguments and their order should be prepared. The same is true about a multi - agent environment where each agent needs planning and executing a sequence of actions to achieve its goals. Some actions in these sequences are under the direct control of the planner agent and others require negotiations with other agents. Negotiation is usually carried out during the plan execution, however, it can be considered during the planning stage, as in real life. In this paper, we present a new approach that takes into account incomplete information about opponent’s position in negotiation by using a nondeterministic planner with incomplete knowlage and sensing actions. We evaluate our approach by solving a well known negotiation scenario.
266
274
N.
Sadeghpoor
A.
Rahati
Autonomous agents
conflict
multi-agent environment
negotiation planning.
Article.5.pdf
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P. Faratin, Automated Service Negotiation Between Autonomous Computational Agents, PhD thesis, University of London, Queen Mary and Westeld College, Department of Electronic Engineering (2000)
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N. C. Karunatillake, Argumentation–Based Negotiation in a Social Context , A thesis submitted in partial fulfillment for the degree of Doctor of Philosophy in the School of Electronics and Computer Science Intelligence, Agents, Multimedia Group (2006)
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Ronald P. A. Petrick, F. Bacchus, A Knowledge-Based Approach to Planning with Incomplete Information and sensing, American Association for Artificial Intelligence, (2004)
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A. Rahati, F. Kabanza , Automated Planning of Tutorial Dialogues, Department of Computer Science University of Sherbrookee Canada. amin.rahati, kabanza@usherbrooke.ca. (2010)
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]
Enhanced Slotted Aloha Mechanism by Introducing Zigzag Decoding
Enhanced Slotted Aloha Mechanism by Introducing Zigzag Decoding
en
en
Various random access mechanisms, such as Aloha protocol and its corresponding variants have been widely studied as efficient methods to coordinate the medium access among competing users. But when two or more wireless users transmit packets at the same time over the same channel a collisions occur. When this happens, the received packets are discarded and retransmissions are required, which is a waste of power and bandwidth. In such a situation one of the most important objectives is to find techniques to improve these protocols to reduce the number of collisions or to avoid them. Several studies have contributed to this problem.
In this paper, we propose a new approach named ZigZag decoding to enhance slotted Aloha mechanism by reducing the loss rate of packets colliding. We model the system by a Markov chain witch the number of backlogged packets is taken as the system state. We use a stochastic game to achieve our objective. We evaluate and compare the performances parameters of the proposed approach with those of slotted Aloha mechanism. All found results show that our approach is more efficient than the slotted Aloha mechanism.
275
285
Abdellah
Zaaloul
Abdelkrim
Haqiq
Slotted Aloha
Markov Process
MAC Protocol
ZigZag Decoding.
Article.6.pdf
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]
Opec Oil Price Prediction Using Anfis
Opec Oil Price Prediction Using Anfis
en
en
In this paper adaptive neuro-fuzzy inference system (ANFIS) is developed to predict the oil prices of the organization of petroleum exporting countries (OPEC). The novel aspect of the proposed model is the proposed features set fed the ANFIS. In the numerical studies, the proposed method is tested to modeling OPEC oil time series as a case study. According to the comparative results, ANFIS with proposed variables set shows higher accuracy than conventional neural networks in oil price prediction.
286
296
E.
Lotfi
M. R.
Karimi
Artificial neural network
Fuzzy
Oil
Forecasting.
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M. Çunkaş, A. A. Altun, Long Term Electricity Demand Forecasting in Turkey Using Artificial Neural Networks, Energy Sources, Part B: Economics, Planning, and Policy, 5(3) (2010), 279-289
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M. G. Deepika, G. Nambiar, M. Rajkumar, Forecasting Price and Analysing Factors influencing the Price of Gold using ARIMA Model and Multiple Regression Analysis, , (2012)
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Ó. Dejuán, L. A. López, M. Á. Tobarra, J. Zafrilla, A POST-KEYNESIAN AGE MODEL TO FORECAST ENERGY DEMAND IN SPAIN, Economic Systems Research, (ahead-of-print), (2013), 1-20
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C. L. Dunis, J. Laws, G. Sermpinis, Modelling and trading the EUR/USD exchange rate at the ECB fixing, The European Journal of Finance, 16(6) (2010), 541-560
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Hamze Ravaee, Saeid Farahat, Faramarz Sarhaddi, Artificial Neural Network Based Model of Photovoltaic Thermal (PVT) Collector , Journal of mathematics and computer Science, 4(3) (2012), 411-417
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I. Haidar, S. Kulkarni, H. Pan, Forecasting Model for Crude Oil Prices Based on Artificial Neural Networks, IEEE ISSNIP, (2008), 103-108
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]
The Proposed Center Initialization Based on Imperialist Competitive Algorithm (cib-ica)
The Proposed Center Initialization Based on Imperialist Competitive Algorithm (cib-ica)
en
en
In this paper we will introduce an initial cluster centers method for k-means algorithm, which can achieve a significant impact on the convergence and will not fall local optimal solution trap .The proposed Center Initialization Based on Imperialist Competitive Algorithm (CIB-ICA) uses imperialist competitive algorithm and minimum spanning tree(MST) features to reduce clustering error percentage of K-means algorithm. The proposed method has been evaluated on some famous datasets and experimental results show that it is an efficient cluster center initialization method.
297
310
Sanaz
Asfia
Arash Ghorbannia
Delavar
data mining
clustering
Imperialist Competitive Algorithm
K-means
center initialization.
Article.8.pdf
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