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2014
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Performance Analysis of Manufacturing Systems Using Deterministic and Stochastic Petri Nets
Performance Analysis of Manufacturing Systems Using Deterministic and Stochastic Petri Nets
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en
Most of manufacturing industries in our country practice the traditional production systems. Effective management of the steady state operation is no longer enough to ensure the survival,let alone the successof an organization. The performance of the operations has to be improved continually in all its aspects, and it is driven by the quest for increased productivity, flexibility and continuously changing competitive environment.The increasing global character of market for goods and services,is stimulated by the factors like improvements in transport, data communication systems, and primarily the automation of manufacturing operations. These features need for continuous performance analysis and improvement of manufacturing systems. Therefore, the key to stay at the apex of global competition is to meet the dynamically changing need of customers. Manufacturing systems performance analysis using Petri Nets(PN) is one of the promising tools employed for assessing. PN models are now common place within the sphere of performance modeling of manufacturing systems due to reasons like graphical and precise representation of system activities and models at various levels of detail and ability to capture the existence of concurrency,parallelism,resource constraints and process dependencies accurately. This paper, focuses on analyzing the performance of the manufacturing process of pars metal, one of the manufacturing industries in the country, using PN so as to evaluate various performance parameters such as utilization rate of machines, bottleneck detection, cycle time,and throughput rate of system under consideration and providing solutions and recommendations for the pitfalls and ramification for attaining the optimum productivity.
1
12
Hassan
Haleh
Arman
Bahari
Behnoosh
Moody
Manufacturing System
Performance Analysis
Stochastic Petri Net
Time Petri Net
Article.1.pdf
[
[1]
P. J. Haas, Stochastic Petri Nets for modeling, , (2004)
##[2]
, , URL: http://www.almaden.ibm.com/cs/people/peterh , ()
##[3]
Elso Kuljanic , Advanced Manufacturing Systems and Technology, SpringerWein, New York (1999)
##[4]
Mesfin Lakew , A petri net approach to bottling line modeling and performance analysis , a case study on Meta Abo brewery Share Company, a thesis submitted to the school of graduate studies of Addis Ababa University in partial fulfillment of the requirements for the degree of Masters of Science in Mechanical Engineering, Addis Ababa University, Addis Ababa (2004)
##[5]
T. Murata, Circuit Theoretic Analysis and Synthesis of Marked Graphs, IEEE Trans. on Circuits and Systems, 24 (1977), 400-405
##[6]
T. Murata, Petri Nets : Properties, Analysis and Applications, Processings of the IEEE, vol. 77, no. 4 (1989)
##[7]
J. Peterson , Petri Net Theory and the Modeling of Systems, Prentice-Hall, Inc. , (1981)
##[8]
J. Richardsson, Development and Verification of Control Systems for Flexible Automation, Licentiate thesis, Control and Automation Laboratory, Chalmers University of Technology, Göteborg, Technical report 015. , Sweden (2005)
##[9]
R. S. Sreenivas, B. H. Krogh , On Petri Net Models of Infinite State Supervisors, IEEE Trans. on Automatic Control, AC-37 (1992), 274-277
##[10]
K. P. Valavanis, Modular Petri Net based Modeling, Analysis, Synthesis and Performance Evaluation of Production Systems, Journal of Intelligent Manufacturing, 16 (2005), 67-92
##[11]
Xiao-rong WANG, Tie-jun WU , Flexible Jobshop Scheduling Based on Petri-net Model, ACO-GA Hierarchical Evolutionary Optimization Approach[J]. Journal of Zhejiang University(Engineering Science), 38(3) (2004), 286-291
##[12]
Hehua Zhang, Ming Gu , Modeling job shop scheduling with batches and setup times by timed Petri nets, Mathematical and Computer Modelling , 49 (2009), 286-294
##[13]
Weiping Zhong , Performance Analysis of Machining Systems with Different Configurations, Proceedings of Japan-USA Symposium on Flexible Manufacturing, Michigan, USA (2000)
##[14]
MengChu Zhou, Kevin McDermott, Paresh A. Patel, Petri Net Synthesis and Analysis of a Flexible Manufacturing System Cell, IEEE Trans. on Systems, Man, and Cybernetics, 23 (1993), 523-531
##[15]
MengChu Zhou, Kurapati Venkatesh , Modeling, Simulation, and Control of flexible Manufacturing System, World Scientific, Hong Kong (1999)
]
Improvement of the Multiquadric Quasi-interpolation \(L_{w_2} \)
Improvement of the Multiquadric Quasi-interpolation \(L_{w_2} \)
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In this paper, we improve the multiquadric (MQ) quasi-interpolation operator \(L_{w_2 }\). The operator \(L_{w_2 }\) is based on inverse multiquadric radial basis function (IMQ-RBF) interpolation, and Wu and Schaback's MQ quasi-interpolation operator \(L_D\). In definition process of the quasi-interpolation \(L_{w_2 }\), the second derivative of function is used that approximated by center finite difference. In this paper, we use compact finite difference for approximation of the second derivative and increase accuracy of quasi-interpolation \(L_{w_2 }\). Numerical experiments demonstrate that the proposed MQ quasi-interpolation scheme is valid.
13
21
Maryam
Sarboland
Azim
Aminataei
Radial basis function
Multiquadric quasi-interpolation
Inverse multiquadric
Compact finite difference.
Article.2.pdf
[
[1]
I. Barrodale, D. Skea, M. Berkley, R. Kuwahara, P. Poeckert, Warping digital images using thin-plate splines, Pattern Recognition , 26 (1993), 375-376
##[2]
R. K. Beatson, M. J. D. Powell, Univariate multiquadric approximation: quasi-interpolation to scattered data, Constr. Approx. , 8 (1992), 275-288
##[3]
J. Biazar, M. Hosami, Two efficient approaches based on radial basis functions to nonlinear time-dependent partial differential equations, J. Math. Computer Sci. , 13 (2014), 1-11
##[4]
J. C. Carr, W. R. Fright, R. K. Beatson, Surface interpolation with radial basis functions for medical imaging, IEEE Trans. Medical Imaging , 16 (1997), 96-107
##[5]
Y. L. Chan, L. H. Shen, C. T. Wu, D. L. Young, A novel upwind-based local radial basis function differential quadrature method for convection-dominated flows, Computer & Fluids , 89 (2014), 157-166
##[6]
R. H. Chen, Z. M. Wu, Solving partial differential equation by using multiquadric quasi-interpolation, Appl. Math. Comput. , 186 (2007), 1502-1510
##[7]
R. H. Chen, Z. M. Wu, Solving hyperbolic conservation laws using multiquadric quasi-interpolation, Numer. Methods Partial Differential Equations , 22 (2006), 776-796
##[8]
B. H. Chovitz, Geodetic theory, Rev. geophys. Space Phys. , 13 (1975), 243-245
##[9]
M. Dehghan, A. Shokri , A numerical method for solution of the two-dimensional Sine-Gordon equation using the radial basis functions, Mathematics and Computers Simulation, 79 (2008), 700-715
##[10]
M. Dehghan, A. Nikpour, Numerical solution of the system of second-order boundary value problems using the local radial basis function based differential quadrature collocation method, Appl. Math. Modelling, 37 (2013), 8578-8599
##[11]
M. Dehghan, A. Shokri, Numerical solution of the nonlinear Klein-Gordon equation using radial basis functions, J. Comput. Appl. Math. , 230 (2009), 400-410
##[12]
M. Dehghan, A. Shokri, A numerical method for KdV equation using collocation and radial basis functions, Nonlinear Dyn., 50 (2007), 111-120
##[13]
F. Gao, Ch. Chi , Numerical solution of nonlinear Burgers' equation using high accuracy multiquadric quasi-interpolation, Appl. Math. Comput. , 229 (2014), 414-421
##[14]
R. L. Hardy, S. A. Nelson, A multiquadric biharmonic representation and approximation of disturbing potential, Geophys. Res. Lett., 13 (1986), 18-21
##[15]
R. L. Hardy, Theory and applications of the multiquadric biharmonic method, Comput. Math. Appl. , 19 (1990), 163-208
##[16]
Y. C. Hon, X. Z. Mao, An efficient numerical scheme for Burgers' equation, Appl. Math. Comput., 95 (1998), 37-50
##[17]
Y. C. Hon, Z. M. Wu, A quasi-interpolation method for solving stiff ordinary differential equations, Internat. J. Numer. Methods Eng. , 48 (2000), 1187-1197
##[18]
J. R. Jancaitis, J. L. Junkins, Modeling irregular surfaces , Photogramm. Engng. , 39 (1973), 413-420
##[19]
Z. W. Jiang, R. H. Wang, C. G. Zhu, M. Xu, High accuracy multiquadric quasi-interpolation, Appl. Math. Modelling , 35 (2011), 2185-2195
##[20]
Z. W. Jiang, R. H. Wang, Numerical solution of one-dimensional Sine-Gordon equation using high accuracy multiquadric quasi-interpolation, Appl. Math. comput. , 218 (2012), 7711-7716
##[21]
E. J. Kansa, Multiquadric-a scattered data approximation scheme with applications to computational fluid dynamics I, Comput. Math. Appl. , 19 (1990), 127-145
##[22]
E. J. Kansa , Multiquadric-a scattered data approximation scheme with applications to computational fluid dynamics II , Comput. Math. Appl. , 19 (1990), 147-161
##[23]
M. Kumar, N. Yadav, Multilayer perceptrons and radial basis function neural network methods for the solution of differential equations: A survey, Comput. Math. With Applications, 62 (2011), 3796-3811
##[24]
W. R. Madych, S. A. Nelson, Multivariate interpolation and conditionally positive defnite functions, Math. Comp., 54 (1990), 211-230
##[25]
K. Parand, S. Abbasbandy, S. Kazem, J. A. Rad, A novel application of radial basis functions for solving a model of first-order integro-ordinary differential equation, Communications in Nonlinear Science and Numerical Simulation , 16 (2011), 4250-4258
##[26]
D. T. Sandwell, Biharmonic spline interpolation of GEOS-3 and SEASAT altimeter data, Geophys. Res. Lett. , 14 (1987), 139-142
##[27]
Z. M. Wu, Dynamically knots setting in meshless method for solving time dependent propagations equation, Comput.Methods Appl. Mech. Eng. , 193 (2004), 1221-1229
##[28]
Z. M. Wu, Dynamically knot and shape parameter setting for simulating shock wave by using multiquadric quasi-interpolation, Engineering Analysis with Boundary Elements, 29 (2005), 354-358
##[29]
Z. M. Wu, R. Schaback, Shape preserving properties and convergence of univariate multiquadric quasi- interpolation, Acta. Math. Appl. Sin. Engl. Ser., 10 (1994), 441-446
##[30]
M. L. Xiao, R. H. Wang, C. H. Zhu , Applying multiquadric quasi-interpolation to solve KdV equation, Mathematical Research Exposition , 31 (2011), 191-201
]
Numerical Method for Solving Optimal Control Problem of Stochastic Volterra Integral Equations Using Block Pulse Functions
Numerical Method for Solving Optimal Control Problem of Stochastic Volterra Integral Equations Using Block Pulse Functions
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In this paper, a numerical method for solving a general optimal control of systems is presented. These systems governed by stochastic Volterra integral equations. This method is based on block pulse functions. By using the properties of block pulse functions and associated operational matrices, optimal control problem is converted to an optimization problem and will be solved via mathematical programming techniques. The error estimations and associated theorems have been provided. Finally, some numerical examples are presented to show the validity and efficiency of the proposed method.
22
36
M.
Saffarzadeh
A.
Delavarkhalafi
Z.
Nikoueinezhad
Stochastic Volterra integral equations
Optimal control
Block pulse functions
Stochastic operational matrix.
Article.3.pdf
[
[1]
A. W. Heemink, I. D. M. Metzelaar, Data assimilation into a numerical shallow water flow model: A stochastic optimal control approach, J. Marine. Syst., 6 (1995), 145-158
##[2]
Y. Shastria, U. Diwekarb, Sustainable ecosystem management using optimal control theory, Part 2 (stochastic systems), J. Theor. Biol. , 241 (2006), 522-532
##[3]
A. J. Coldman, J. M. Murray, Optimal control for a stochastic model of cancer chemotherapy, Math. Biosci. , 168 (2000), 187-200
##[4]
J. L. Stein, Applications of stochastic optimal control/dynamic programming to international finance and debt crises, Nonlinear. Anal., 63 (2005), 1-2033
##[5]
J. M. Petersen, M. A. Petersen , Bank management via stochastic optimal control, Automatica, 42 (2006), 1395-1406
##[6]
S. U. Acikgoz, U. M. Diwekar, Blood glucose regulation with stochastic optimal control for insulin-dependent diabetic patients, Chem. Eng. Sci. , 65 (2010), 1227-1236
##[7]
P. T. Benavides, U. Diweka, Optimal control of biodiesel production in a batch reactor , Part II: Stochastic control, Fuel. , 94 (2012), 218-226
##[8]
W. H. Fleming, C. J. Rishel, Deterministic and Stochastic Optimal Control, Springer-Verlag, (1975)
##[9]
W. H. Fleming, H. M. Soner, Controlled Markov Processes and Viscosity Solutions, Springer, (2006)
##[10]
R. E. Bellman, Dynamic Programming, Princeton University Press, Princeton, NJ (1957)
##[11]
R. E. Bellman, S. E. Dreyfus, Applied Dynamic Programming, Princeton University Press, Princeton, NJ ( 1962)
##[12]
D. E. Kirk , Optimal Control Theory An Introduction, Prentice-Hall , Englewood Cliffs (1970)
##[13]
A. B. Pantelev, A. C. Bortakovski, T. A. Letova, Some Issues and Examples in Optimal Control, MAI Press , Moscow (in Russian) (1996)
##[14]
E. R. Pinch, Optimal Control and the Calculus of Variations, Oxford University Press, London (1993)
##[15]
L. S. Pontryagin, The Mathematical Theory of Optimal Processes, Interscience, John Wiley and Sons (1962)
##[16]
A. Jajarmi, N. Pariz, S. Effati, A. V. Kamyad, Infinite horizon optimal control for nonlinear interconnected Large-Scale dynamical systems with an application to optimal attitude control, Asian. J. Control. , 15 (2013), 1-12
##[17]
B. Kafash, A. Delavarkhalafi, S. M. Karbassi, Application of variational iteration method for Hamilton–Jacobi–Bellman equations, Appl. Math. Modell., 37 (2013), 3917-3928
##[18]
H. M. Jaddu, Direct solution of nonlinear optimal control problems using quasilinearization and Chebyshev polynomials, J. Franklin Inst., 39 (2002), 479-498
##[19]
B. Kafash, A. Delavarkhalafi, S. M. Karbassi, Application of Chebyshev polynomials to derive efficient algorithms for the solution of optimal control problems, Sci. Iran., 19 (2012), 795-805
##[20]
K. Maleknejad, H. Almasieh, Optimal control of Volterra integral equations via triangular functions, Math. Comput. Modell., 53 (2011), 1902-1909
##[21]
H. R. Erfanian, M. H. Noori Skandari, Optimal control of an HIV model, The Journal of Mathematics and Computer Science , 2 (2011), 650-658
##[22]
E. Hesameddini, A. Fakharzadeh Jahromi, M. Soleimanivareki, H. Alimorad, Differential transformation method for solving a class of nonlinear optimal control problems , The Journal of Mathematics and Computer Science , 5 (2012), 67-74
##[23]
C. Myers, Stochastic Control, Sciyo, Croatia (2010)
##[24]
B. Øksendal, T. Zhang, Optimal control with partial information for stochastic Volterra equations, Int. J. Stoch. Anal. Art. ID 329185, (2010), 1-25
##[25]
S. Ji, X. Y. ZHOU, A maximum principle for stochastic optimal control with terminal state constraints and its applications , Commun. Info. Sys. , 6 (2006), 321-338
##[26]
Z. Wu, A general maximum principle for optimal control of forward-backward stochastic systems, Automatica, 49 (2013), 1473-1480
##[27]
S. Bonaccorsi, F. Confortola, E. Matrogiacomo, Optimal control for stochastic Volterra equations with completely monotone kernels, Siam. J. Control. Optim. , 50 (2012), 748-789
##[28]
N. Kuchkina, L. Shaikhet, Optimal control of Volterra Type stochastic difference equations , Comput. Math. Appl. , 36 (1998), 251-259
##[29]
H. J. Kushner, Numerical methods for stochastic control problems in finance, Lefschetz Center for Dynamical Systems and Center for Control Sciences, Division of Applied Mathematics, Brown University (1995)
##[30]
C. Munk, Numerical methods for continuous-time, continuous-state stochastic control problems, Publications from department of management , 97, No. 11 (1997)
##[31]
C. Munk , The Markov chain approximation approach for numerical solution of stochastic control problems: experiences from Merton’s problem, Appl. Math. Comput. , 136 (2003), 47-77
##[32]
W. Chavanasporn, C. O. Ewald, A numerical method for solving stochastic optimal control problems with linear control, Comput. Econ., 39 (2012), 429-446
##[33]
K. Maleknejad, M. Khodabin, M. Rostami, Numerical solution of stochastic Volterra integral equations by a stochastic operational matrix based on block pulse functions, Math. Comput. Modell., 55 (2012), 791-800
##[34]
G. P. Rao, Piecewise constant orthogonal functions and their application to systems and control, Springer, Berlin (1983)
##[35]
Z. H. Jiang, W. Schaufelberger, Block pulse functions and their applications in control systems, Springer-Verlag, (1992)
##[36]
K. Maleknejad, M. Khodabin, M. Rostami, A numerical method for solving m-dimensional stochastic Itô-Volterra integral equations by stochastic operational matrix, Comput. Math. Appl. , 63 (2012), 133-143
##[37]
M. Khodabin, K. Maleknejad, M. Rostami, M. Nouri , Numerical approach for solving stochastic Volterra-Fredholm integral equations by stochastic operational matrix, Comput. Math. Appl. , 64 (2012), 1903-1913
##[38]
D. J. Higham, An algorithmic introduction to numerical simulation of stochastic differential equations, Siam Rev. , 43 (2001), 525-546
##[39]
J. Engwerda, LQ Dynamic Optimization and Diffrential Games, John Wileyn and Sons LTD, (2005)
]
Natural Map in Feblins Type Fuzzy Normed Linear Spaces
Natural Map in Feblins Type Fuzzy Normed Linear Spaces
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en
In this paper, we aim to present some properties of the space of all weakly fuzzy bounded linear operators, with the Bag and Samanta's operator norm on fuzzy normed linear spaces. We introduced the natural linear injection of a fuzzy normed linear space X in to its second dual spaceX".
37
41
A.
Zohourmeskar
Fuzzy real number
Fuzzy norm linear space
Weakly fuzzy continuouse
Weakly fuzzy bounded.
Article.4.pdf
[
[1]
T. Bag, S. K. Samanta, Fuzzy bounded linear operators in Felbin’s type normed linear spaces, Fuzzy sets and Systems , 159 (2008), 685-707
##[2]
D. Dubouis, H. Prade, Operations on Fuzzy numbers, International Journal of System, 9 (1978), 613-626
##[3]
C. Felbin, Finite dimensional fuzzy normed linear spaces, fuzzy set and System, 48 (1992), 239-248
##[4]
C. Felbin, Finite dimensional fuzzy normed linear spaces II, J. Analysis, 7 (1999), 117-131
##[5]
O. Kaleva, S. Seikkala, On fuzzy metric spaces, Fuzzy set and System, 12 (1984), 215-229
##[6]
I. Sadeqi, M. Salehi, Fuzzy compact operators and topological degree theory, Fuzzy set and Systems , 160 (2009), 1277-1285
##[7]
A.Taghavi, M. Mehdizadeh, Adjoint operator in fuzzy normed linear spaces, The Journal of Mathematics and Computer Science, 3 (2011), 453-458
##[8]
J. Xiao, X. Zhu, On linearly topological structure and property of fuzzy normed linear space , Fuzzy Sets and Systems, 125 (2002), 153-161
]
Design an Optimal T-s Fuzzy Pi Controller for a Non-inverting Buck-boost Converter
Design an Optimal T-s Fuzzy Pi Controller for a Non-inverting Buck-boost Converter
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en
In this paper, first, the different operation modes of a non–inverting buck–boost converter are examined, and then an optimal T-S fuzzy PI controller is proposed to control the converter under variable reference voltages. The controller is designed based on input-output pairs of the classic PI controller to employ both conscious and subconscious knowledge. For this aim, the initial fuzzy system generated by subtractive clustering method and then the Recursive Least Mean Square (RLS) is used to adjust the coefficients of consequent part of fuzzy rules. Simulation and experimental results show the superior control performance of the fuzzy PI controller over the classic PI controller.
42
52
Omid Naghash
Almasi
Vahid
Fereshtehpoor
Abolfazl
Zargari
Ehsan
Banihashemi
Fuzzy PI controller
Non–inverting Buck–boost Converter
TSK fuzzy systems.
Article.5.pdf
[
[1]
B. Sahu, G. A. Rincon-Mora, A Low Voltage, Dynamic, Non-inverting, Synchronous Buck-Boost Converter for Portable Applications, IEEE Trans. Power Electron. , 19 (2004), 443-452
##[2]
H. Xiao, S. Xie , Interleaving double-switch buck-boost converter, IET Power Electron, (2012), 899-908
##[3]
R. F. Coelho, F. M. Concer, D. C. Martins , Analytical and Experimental Analysis of DC-DC Converters in Photovoltaic Maximum Power Point Tracking Applications, In IECON 2010-36th Annual Conference on IEEE Industrial Electronics Society, (2010), 2778-2783
##[4]
V. Fereshtehpoor , Improvement in Power Factor Correction Capability in the Single Phase Non-inverting Buck-Boost Converter, Master’s science thesis, Dept. Electrical Eng., Science and Research Branch of Islamic Azad University, Tehran (2012)
##[5]
Y. Lee, A. Khaligh, A. Emadi , A Compensation Technique for Smooth Transitions in a Non-inverting Buck–Boost Converter, IEEE Trans. Power Electron, 24 (2009), 1002-1015
##[6]
E. Schaltz, P. O. Rasmussen, A. Khaligh, Non-Inverting buck-boost converter for fuel cell applications, In Industrial Electronics, IECON 2008. 34th Annual Conference of IEEE, (2008), 855-860
##[7]
N. Mohan, T. M. Undeland, W. P. Robbins, Power Electronics Converters, Applications, and Design, J. Wiley, 3rd ed., (2003)
##[8]
R. D. Middlebrook, S. R. Cuk , A general unified approach to modeling switching converter power stages, In Power Electronics Specialists Conference, 1 (1976), 18-34
##[9]
T. Takagi, M. Sugeno, Fuzzy identification of systems and its applications to modeling and control, IEEE Trans. Systems Man Cybernet, 15 (1985), 116-132
##[10]
W. L. Xin, A Course in Fuzzy Systems and Control, Englewood Cliffs, NJ: Prentice-Hall (1996)
##[11]
S. Guillaume , Designing fuzzy inference systems from data: An interpretability-oriented review, IEEE Trans. Fuzzy Sys., 9 (2001), 426-443
##[12]
C. Hung, L. Huang, Extracting Rules from Optimal Clusters of Self-Organizing Maps, Int.Conf. on Computer Modeling and Simulation, 1 (2010), 382-386
##[13]
M. Setnes, Supervised fuzzy clustering for rule extraction, IEEE Trans. fuzzy sys. , 8 (2000), 416-424
##[14]
A. Priyono, M. Ridwan, A. J. Alias, R. A. OK Rahmat, A. Hassan, M. A. Mohd Ali, Generation of fuzzy rules with subtractive clustering, Journal Technology, 43 (2012), 143-153
##[15]
C. Restrepo, J. Calvente, A. Cid-Pastor, A. E. Aroudi, R. Giral, A Noninverting Buck–Boost DC–DC Switching Converter With High Efficiency and Wide Bandwidth, IEEE Trans. Power Electron, 26 (2011), 2490-2503
##[16]
H. K. Lam, S.-C Tan, Stability analysis of fuzzy-model-based control systems: application on regulation of switching DC-DC converter, IET Control Theory & Applications, 3 (2009), 1093-1106
##[17]
R. Erickson, D. Maksimovic, Fundamental of power electronics, Springer, 2nd ed. (2000)
##[18]
J. J. Buckley, Sugeno type controllers are universal controllers, Fuzzy Sets and Systems, 53 (1993), 299-303
##[19]
R. Qi, M. A. Brdys, Stable indirect adaptive control based on discrete-time T–S fuzzy model, FuzzySets and Systems, 159 (2008), 900-925
##[20]
S. Chiu, Extracting fuzzy rules from data for function approximation and pattern classification, Fuzzy Information Engineering: A Guided Tour of Application. D. Dubois, H. Prade and R. Yager(eds), John Wiley Sons (1997)
##[21]
K. Ogata, Modern control engineering, Prentice Hall, 5th ed. (2010)
]
Fairness Aware Downlink Scheduling Algorithm for Lte Networks
Fairness Aware Downlink Scheduling Algorithm for Lte Networks
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en
This paper proposes a new scheduling algorithm in downlink Long Term Evolution networks which is adoptable with fast variations in channel conditions.It allocates resources in a fair manner among users so that it increases cell edge users’ performance. Besides, it makes a good trade-off between throughput and fairness. The proposed method is simulated and compared with three scheduling algorithms available for Long Term Evolution. The results showgood degree of fairness at the cost of small decrease in amount of system throughput.
53
63
Mahnaz Sotoudeh
Bahreyni
Vahid
Sattari-naeini
Downlink
Radio Resource Allocation
Fairness
LTE.
Article.6.pdf
[
[1]
A. Ghosh, R. Ratasuk, B. Mondal, N. Mangalvedheand, T. Thomas, LTE-Advanced: next-generation wireless broadband technology, ACM Transactions on Multimedia Computing, Communications And Applications, 17 (2010), 10-22
##[2]
M. Sauter, From GSM To LTE: An introduction to mobile networks and mobile broadband, John Wiley &Sons Ltd., (2011)
##[3]
R. Van Nee, R. Prasad , OFDM for wireless multimedia communications, Artech House Publications, (2000)
##[4]
H. Holma, A. Toskala, LTE for UMTS–OFDMA and SC-FDMA based radio access, John Wiley &Sons Ltd., (2009)
##[5]
G. L. Stuber, J. R. Barry, S. W. Mclaughlin, Y. Li, M. A. Ingram, T. G. Pratt, Broadband MIMO-OFDM wireless Communications, Proceedings of IEEE, 92 (2004), 271-294
##[6]
A. C. B. Akki, S. M. Chadcha, Fair downlink scheduling algorithm for 3gpp LTE networks, I., J. Computer Network and Information Security, 6 (2013), 34-41
##[7]
P. Kela, J. Puttonen, N. Kolehmainen, T. Ristaniemi, T. Henttonen, M. Moisio, Dynamic packet scheduling performance in utra long term evolution downlink, In Proceedings of the 3rd International Symposium on Wireless Pervasive Computing (ISWPC 2008), (2008), 308-313
##[8]
D. Talevski, L. Gavrilovska, Novel scheduling algorithms for LTE downlink transmission, Telfor Journal , 4 (2012), 24-25
##[9]
T. Dikamba , Downlink Scheduling In 3GPP Long Term Evolution (LTE), M.Sc. Thesis, Delft University of Technology, Netherlands (2011)
##[10]
R. Kwan, C. Leung, J. Zhang, Proportional fair multiuser scheduling in LTE, Proceedings of IEEE Signal Processing Letters, 16 (2009), 461-464
##[11]
G. Monghal, K. I. Pedersen, I. Z. Kovacs, P. E. Mogensen, Qos oriented time and frequency domain packet schedulers for the UTRAN long term evolution, In Proceedings of IEEE Veh. Tech. Conf., VTC-Spring, Marina Bay, Singapore (2008)
##[12]
Y. Lin, G. Yue, Channel-Adapted and buffer-aware packet scheduling in LTE wireless communication system, In Proceedings of the IEEE International Conference on Wireless Communications, Networking and Mobile Computing (Wicom’08) (2008)
##[13]
D. Mannani, Modeling and simulation of scheduling algorithms in LTE networks, B.sc Thesis,Electrical and Computer Engineering, The Institute Of Telecommunications, Faculty Of Electronics and Information Technology, Warsaw University of Technology, Warsaw ( 2012)
##[14]
M. H. Habaebi, J. Chebil, A. G. Al-Sakkaf, T. H. Dahawi, comparison between scheduling techniques in long term evolution, IIUM Engineering Journal , Vol. 14 No. 1 (2013)
##[15]
E. Dahlman, S. Parkvall, J. Skold, P. Beming, 3G Evolution: HSPA And LTE for mobile broadband, 2nd Edition , Academic Press (2008)
##[16]
R. Jain, The art of computer systems performance analysis: techniques for experimental design, measurement, simulation and modeling, John Wiley &Sons, New York (1991)
##[17]
, LTE System Level Simulator, Institute Of Communication And Radio Frequency Engineering, Vienna University Of Technology, Vienna ()
##[18]
M. Nejadkheirallah, M. M. Tajari, R. Sookhtsaraei, A. Yousefzadeh , Multi-hop Fuzzy Routing for Wireless Sensor Network with Mobile Sink, Journal of mathematics and computer science, 9 (2014), 12-24
]
A Rough Set Based Approach to Classify Node Behavior in Mobile Ad Hoc Networks
A Rough Set Based Approach to Classify Node Behavior in Mobile Ad Hoc Networks
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en
Mobile Ad Hoc Network are used in places where providing a network infrastructure is difficult. In Ad
Hoc Network the mobile nodes are not controlled by any other controlling entity, they have
unrestricted mobility and form the dynamic topology. This dynamically changing network topology of
MANETs makes it vulnerable to many security related issues. There are some situations when one or
more nodes in the network become selfish or malicious and tend to annihilate the capacity of the
network. This research investigate the classification of good and bad nodes in the network by using the
concept of rough set theory , that can be employed to generate simple rules and to remove irrelevant
attributes for discerning the good nodes from bad nodes. Our experiment results reveals that the rough
set based approach increases the network capacity and throughput of the network up to 98.9%.
64
78
M.
Jain
M. P. S.
Bhatia
Indiscernibility
Equivalence Class
Reducts
Malicious Nodes
Rough Sets
Crisp Sets
Decision Rules.
Article.7.pdf
[
[1]
Ehsan Hemmati, Mansour Sheikhan, Reliable disjoint path selection in Mobile ad hoc network using noisy hop field neural network , 5th International symposium on Telecommunications, (2010)
##[2]
Uman Singh, B. V. R. Reddy, M. N.Hoda, GNDA: Detecting good neighbor nodes in ad hoc routing protocol, Second International Conference on Emerging Applications of Information Technology, (2011)
##[3]
Jan Komorowski, Lech Polkowski, Andrej Skowron, Rough Sets: A Tu., , ()
##[4]
Srdjan Krco, Marina Dupcinov, Improved Neighbor Detection Algorithm for AODV Routing Protocol, IEEECOMMUNICATIONS LETTERS, VOL 7, NO. 12, DECEMBER (2003)
##[5]
Youngrag Kim, Shuhrat Dehkanov, Heejoo Park, Jaeil Kim, Chonggun Kim, The Number of Necessary Nodes for Ad Hoc Network Areas ”,, IEEE Asia-Pacific Services Computing Conference. , (2007)
##[6]
S. Marti, T. J. Giuli, K. Lai, M. Baker, Mitigating Routing Misbehavior in Mobile Ad Hoc Networks, in 6th International Conference on Mobile computing and Networking, MOBICOM’00, (2000), 255-265
##[7]
A. Hasswa, M. Zulker, H. Hassanein, Route guard: an intrusion detection and response system for mobile ad hoc networks, Wireless and Mobile Computing, Networking and Communication , 3 (2005), 336-343
##[8]
C. Manikopoulos, Li Ling, Architecture of the mobile ad hoc network security (MANS) system, in: Proceedings of the IEEE International conference on Systems. Man and Cybernetics, 4 (2003), 3122-3127
##[9]
Y. Zhang, W. Lee, Intrusion Detection in Wireless ad hoc Networks, Mobicom 2000, August 6-11, Boston,Massachusetts, USA (2000)
##[10]
RSES , Homepage http://logic.mimuw.edu.pl/»rses , , ()
##[11]
, NS by Example, http://nile.wpi.edu/NS/overview.html, 14 May (2006)
##[12]
K. Fall, K. Varadhan, The NS Manual, November 18, http://www.isi.edu/nsnam/ns/doc/ns_doc.pdf. , 25 July (2005)
##[13]
M. Riki, H. Rezaei, Introduction of rough set theory and Application in Data analysis, Journal of Mathematics and Computer Science , 9 (2014), 25-32
]
A Hierarchical Pso Algorithm for Solving Linear Trilevel Programming Problems
A Hierarchical Pso Algorithm for Solving Linear Trilevel Programming Problems
en
en
Trilevel programming deals with hierarchical optimization problems that in which the top-level, middle-level and bottom-level decision-makers attempt to optimize their individual objectives, but their decisions are affected by the optimal objective values presented at other levels. In this paper, we propose a hierarchical particle swarm optimization (PSO) method for solving linear trilevel programming problems (LTLPPs). The proposed method, solves the top-level, middle-level and bottom-level problems iteratively by three variants of PSO. Finally, we give some illustrative examples to show the efficiency of the proposed algorithm.
79
85
Habibe
Sadeghi
Maryam
Esmaeili
Bilevel programming
Trilevel Programming
Particle Swarm Optimization.
Article.8.pdf
[
[1]
G. Anandalingam, T. Fries , Hierarchical Optimization: an introduction, Annals of Operations Research, 34 (1992), 1-11
##[2]
J. Bard, An investigation of the linear three level programming problems, IEEE Transactionson systems, Man and Cybernetics, 14 (1984), 711-717
##[3]
J. Bard, Practical Bilevel Optimization, Algorithms and Applications, Kluwer Academic Publishers, Dordrecht, London (1998)
##[4]
H. Benson, On the structure and properties of a linear multilevel programming problem, Journal of Optimization Theory and Application, 9 (1989), 353-373
##[5]
W. Candler, R. J. Townsley, A linear multilevel programming problem, Computer and operations Research, 9 (1982), 59-67
##[6]
D. Cao, M. Chen, Capacitated plant selection in decentralized manufacturing environment: A bilevel optimization approach, European journal of operational research, 169 (2006), 97-110
##[7]
C. Feng, C. Wen, Bilevel and multi objective model to control trafficflow into the disaster area, post-earthquake , Journal of the EasternAsia Society for Transportation Studies, 6 (2005), 4253-4268
##[8]
S. R. Hejazi , Methods for solving Linear Multilevel Programming Problems, A Thesis Presented for the Degree of Ph.D. in Industrial Engineering, School of Engineering, Tarbiat Modarress University, Iran (2001)
##[9]
J. Kennedy, R. C. Eberhart , Particle Swarm Optimization, IEEE International Conferenceof NeuralNetworks, Perth, Australia, (1995), 1942-1948
##[10]
X. Li, P. Tian, X. Min, A Hierarchical Particle Swarm Optimization for Solving Bilevel Programming Problems, Lecture Notes in Computer Science, 4029 (2006), 1169-1178
##[11]
R. Shakerian, S. H. Kamali, M. Hedayati, M. Alipour, Comparative Study of Ant Colony Optimization and Particle Swarm optimization for Gris Scheduling, TJMCS , 3 (2011), 469-474
##[12]
Y. Shi, R. C. Eberhart, A Modified Particle Swarm Optimizer, IEEE International Conference of Evolutionary Computation, (1998)
##[13]
Y. Shi, R. C. Eberhart, Empirical Study of Particle Swarm Optimization, In Proceeding of the Congress on Evolutionary Computation, (1999), 1945-1949
##[14]
P. N. Suganthan, Particle Swarm Optimizer with Neighborhood Operator, Congress on Evolutionary Computation, Washington, (1999), 1958-1962
##[15]
U. Wen, W. Bialas, The hybrid algorithm for solving the three level programming problem, Computer and Operation Research, 13 (1986), 367-377
##[16]
G. Zhang, J. Lu, J. Montero, Y. Zeng, Model, solution concept, and K-th Best algorithm for linear trilevel programming, Information Sciences, 180 (2010), 481-492
]