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2012
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Solving a Class of Nonlinear Optimal Control Problems by Differential Transformation Method
Solving a Class of Nonlinear Optimal Control Problems by Differential Transformation Method
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en
Based on the Differential Transformation Method (DTM), a solution procedure for solving a class of nonlinear quadratic optimal control problems is presented in this paper. The reason for selecting this solution procedure is the less computational cost in comparison with the ordinary solution methods of original problem. First, the problem is converted to a two-point boundary value problem then the new problem is transferred into a set of algebraic equations by applying the differential transformation properties. By presenting the algorithmic solution procedure, two numerical examples are given to demonstrate the simplicity and efficiency of the new method.
146
152
A.
Fakharzadeh
S.
Hashemi
Quadratic optimal control problems
Pontryagin’s maximum principle
Differential transformation method
recursive relations
Article.1.pdf
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S. Wei, M. Zefran, R. A. DeCarlo, Optimal control of robotic system with logical constraints: application to UAV path planning, In Proceeding(s) of the IEEE International Conference on Robotic and Automation, Pasadena, CA, USA, (2008), 249-256
##[4]
M. Shirazian, S. Effati, Solving a Class of Nonlinear Optimal Control Problems via He’s Variational Iteration Method, International Journal of Control, Automation, and Systems (2012)
##[5]
S. Effati, H. Saberi Nic, Solving a class of linear and non-linear optimal control problems by homotopy, IMA Journal of Mathematical Control and Information, 28 (2011), 539-553
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A. Jajarmi, N. Pariz, A. VahidianKamyad, A highly computational efficient method to solve nonlinear optimal control problems, ScientiaIranica, (2011)
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R. Abazari, R. Abazari, Numerical study of some coupled PDEs by usingdifferential transformation method, Engineering and Technology 66Management, (2010)
]
Nonlinear Programming Model for the Facility Location Problem in the Presence of Arc-shaped Barrier
Nonlinear Programming Model for the Facility Location Problem in the Presence of Arc-shaped Barrier
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en
In this paper we consider the single facility location problem with respect to a given set of existing facilities in the presence of an arc-shaped barrier. A barrier is considered a region where neither facility location nor travelling is permitted. We present a mixed-integer nonlinear programming model for this single facility location problem. The objective of this problem is to locate this single facility such that the sum of the rectilinear distances from the facility to the demand points is minimized. Test problems are presented to illustrate the applicability of the proposed model.
153
159
Fatemeh
Akbari
Saeed
Akbari
Iraj
Mahdavi
Saber
Shiripour
Facility location problem
Arc-shaped barrier
Mixed-integer nonlinear programming model
rectilinear distance
Article.2.pdf
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[1]
H. Kelachankuttu, R. Batta, R. Nagi, Contour line construction for a new rectangular facility in an existing layout with rectangular departments, European Journal of Operational Research., 180 (2007), 149-162
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Y. P. Aneja, M. Parlar, Algorithms for Weber facility location in the presence of forbidden and or barriers to travel, Transportation Science, 28 (1994), 70-76
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S. J. Wang, J. Bhadury, R. Nagi, Supply facility and input/output point locations in the presence of barriers , Computers and Operations Research, 29 (2002), 685-699
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P. Nandikonda, R. Batta, R. Nagi, Locating a 1-center on a Manhattan plane with ‘arbitrarily’ shaped barriers, Annals of Operations Research, 123 (2003), 157-172
##[7]
K. Klamroth, A reduction result for location problems with polyhedral barriers, European Journal of Operational Research, 130 (2001), 486-497
##[8]
K. Klamroth, Algebraic properties of location problems with one circular barrier, European Journal of Operational Research, 154 (2004), 20-35
##[9]
M. S. Canbolat, G. O. Wesolowsky , The rectilinear distance Weber problem in the presence of a probabilistic line barrier, European Journal of Operational Research, 22 (2010), 114-121
]
A Neural Network Approach to Solve Semi-infinite Linear Programming Problems
A Neural Network Approach to Solve Semi-infinite Linear Programming Problems
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en
In this article, we present a new algorithm for solving Semi-Infinite Linear Programming (SILP) problems based on an artificial neural network concept. First the local reduction method for solving the SILP problems is introduced. Based on the local reduction method, the Karush-Kuhn-Tucker (KKT) conditions and gradient method are used to convert the SILP problem to an unconstrained optimization problem; then, a neural network model is constructed to solve it. Numerical example has been employed to indicate the accuracy of the new method.
160
166
Alireza
Fakharzadeh
Zahra
Alamdar
Masoumeh
Hosseinipour
Semi-Infinite linear programming
Neural network
Local reduction method
KKT conditions.
Article.3.pdf
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]
Manor for Control of the Car Park Using Fuzzy Logic
Manor for Control of the Car Park Using Fuzzy Logic
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en
In this article we have tried to control your car and park it was good. the car park there are different methods such as using fuzzy controller, genetic algorithm, neural networks, image processing, sensors and .... use In this paper, fuzzy control method is used. Park for three types of controllers is proposed in this paper. for park in the rear and park in front of the fuzzy controller if the primary controller having a first car or the car park for the start of the second controller in the output. the second controller uses the vehicle away from the table and change the angle of rotation angle of the car makes the car move. park is also only a single controller for the vertical input, single output is used to move the vehicle entrance and exit angle of the vehicle steering angle is changed. this has the advantage over other papers that extra controller to start the car park used in most papers on this subject has been addressed.
167
175
Jamal
Ghobadi Dizaj Yekan
Amin Adineh
Ahari
Seyed Kamaleddin
Mousavi Mashhadi
Estimated
The rules
Fuzzy controller
Parallel Park The membership
Article.4.pdf
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R. M. De Santis, R. Hurteau, O. Alboui, B. Lesot, Experimental Stabilization of Tractor and Tractor-Trailer like Vehicles, Proc. of the 2002 IEEE Int. Sym. on Intelligent Ctrl., (2002), 188-193
##[2]
W. Young, R. G. Thompson, M. A. P. Taylor, A review of urban parking models, Transport Reviews , 11 (1991), 1-6384
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R. J. Oentaryo, M. Pasquier, Self-Trained Automated Parking System, in 2004 8th International Conference on Control, Automation, Robotics and Vision (ICARCV) (2004)
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A. KHOUKHI, L. BARON, M. BALAZINSKI, Fuzzy Parking Manoeuvres of Wheeled Mobile Robots , 1-4244-1214-5/07/$25.00 ©2007 IEE., ()
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Chen-Kui Lee, Chun-Liang Lin, Bing-Min Shiu , Autonomous Vehicle Parking Using Artificial Intelligent Approach, 978-1-4244-2713-0/09/,25.00 ©2009 IEEE. , ()
##[19]
Benjamas Panomruttanarug, Sarayut Tungporntawee, Parnupong Thongsuk, Kohji Higuchi , An Emulation of Autonomous Parallel Parking System Using Fuzzy Logic Control , PR0002/09/0000-4548 ¥400 © 2009 SICE. , ()
##[20]
Zhi-Long Wang, Chih-Hsiung Yang, Tong-Yi Guo , The Design of An Autonomous Parallel Parking Neuro-Fuzzy Controller for A Car-like Mobile Robot , PR0001/10/0000-2593 ¥400 © 2010 SICE., ()
]
Best Minimizing Algorithm for Shape-measure Method
Best Minimizing Algorithm for Shape-measure Method
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en
The Shape-Measure method for solving optimal shape design problems (OSD) in cartesian coordinates is divided into two steps. First, for a fixed shape (domain), the problem is transferred to the space of positive Radon measures and relaxed to a linear programming in which its optimal coefficients determine the optimal pair of trajectory and control. Then, a standard minimizing algorithm is used to identify the best shape. Here we deal with the best standard algorithm to identify the optimal solution for an OSD sample problem governed by an elliptic boundary control problem.
176
184
Alireza
Fakharzadeh
Zahra
Rafiei
elliptic equation
Radon measure
optimal shape
search techniques.
Article.5.pdf
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O. Bozorg Haddad, A. Afshar, M. A. Marino, Honey-Bees Mating Optimization (HBMO) Algorithm:A New Heuristic Approach for Water Resource Optimization, Water Resources Management, 20 (2006), 661-680
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E. D. Dolan, Pattern Search Behaviour in Nonlinear Optimization, Phd Thesis from the College of William-Mary in Virginia , (1999)
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A. E. Eiben, E. H. Aarts, K. M. VanHee , Global convergence of jenetic algorithms: A markov chain analysis, Department of Mathematics and Computing Science, Eindhoven University of Technology, springer, (1991), 4-12
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A. Fakharzade, J. E. Rubio, Best domain for an elliptic problem in cartesian coordinates by Means of shape-measure, AJOP Asian, J. of control, 11 (2009), 536-554
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M. Gen, R. Cheng, Genetic Algorithm and Engineering Design, John Wiley and Sons, (1997)
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L. Han, M. Neumann, Effect of dimensionality on the Nelder-Mead simplex method, Optimization Methods and Software, 21 (2006), 1-16
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T. G. Kolda, R. M. Lewis, V. J. Torczon, Optimization by direct search: New perspectives on some classical and modern methods, SIAM Review, 45 (2003), 385-482
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]
Solving Nonlinear Fractional Differential Equations by Bernstein Polynomials Operational Matrices
Solving Nonlinear Fractional Differential Equations by Bernstein Polynomials Operational Matrices
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en
In this paper, we solve nonlinear fractional differential equations by Bernstein polynomials. Firstly, we derive the Bernstein polynomials (BPs) operational matrix for the fractional derivative in the Caputo sense, which has not been undertaken before. This method reduces the problems to a system of algebraic equations. The results obtained are in good agreement with the analytical solutions and the numerical solutions in open literatures. Also, the solutions approach to classical solutions as the order of the fractional derivatives approach to 1.
185
196
Mohsen
Alipour
Davood
Rostamy
Nonlinear fractional differential equations
Bernstein polynomials
operational matrix
Caputo derivative
Article.6.pdf
[
[1]
X. Gao, J.Yu , Synchronization of two coupled fractional-order chaotic oscillators, Chaos Sol. Fract. , 26 (1) (2005), 141-145
##[2]
J. G. Lu, Chaotic dynamics and synchronization of fractional-order Arneodo’s systems, Chaos Sol. Fract., 26 (4) (2005), 1125-1133
##[3]
J. G. Lu, G. Chen, A note on the fractional-order Chen system, Chaos Sol. Fract., 27 (3) (2006), 685-688
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I. Podlubny, Fractional Differential Equations, Academic Press, NewYork (1999)
##[5]
S. Momani, Z. Odibat, Homotopy perturbation method for nonlinear partial differential equations of fractional order, Phys. Lett. A , 365 (5-6) (2007), 345-350
##[6]
S. Momani, Z. Odibat, Numerical comparison of methods for solving linear differential equations of fractional order, Chaos Soliton. Fract. , 31 (5) (2007), 1248-1255
##[7]
Z. Odibat, S. Momani, Modified homotopy perturbation method: application to quadratic Riccati differential equation of fractional order, Chaos Soliton. Fract. , 36 (1) (2008), 167-174
##[8]
M. Zurigat, S. Momani, Z. Odibat, A. Alawneh, The homotopy analysis method for handling systems of fractional differential equations, Applied Mathematical Modelling , 34 (2010), 24-35
##[9]
S. Momani, Al-Khaled, Numerical solutions for systems of fractional differential equations by the decomposition method, Appl. Math. Comput., 162 (3) (2005), 1351-1365
##[10]
H. Jafari, V. D. Gejji, Solving a system of nonlinear fractional differential equations using Adomain decomposition, Appl. Math. Comput., 196 (2006), 644-651
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V. Daftardar-Gejji, H. Jafari, Adomian decomposition: a tool for solving a system of fractional differential equations, J. Math. Anal. Appl., 301 (2) (2005), 508-518
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Z. Odibat, S. Momani, Application of variational iteration method to nonlinear differential equations of fractional order, Int. J. Nonlinear Sci. Numer. Simulat, 1 (7) (2006), 15-27
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V. Daftardar-Gejji, H. Jafari, An iterative method for solving nonlinear functional equations, J. Math. Anal. Appl., 316 (2006), 753-763
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V. S. Ertürk, S. Momani, Solving systems of fractional differential equations using differential transform method, Journal of Computational and Applied Mathematics , 215 (2008), 142-151
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]
Incubator with Fuzzy Logic
Incubator with Fuzzy Logic
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en
Today by increasing population growth, poultry farming has found an important place , one of these solution for poultry farming is to using incubator . Today one the main methods in incubation industry that works smart , this system can be fuzzy logic. In this article we plan to study getting fuzzy of three parameter such as temperature, moisture and oxygen that they have an effective role in the incubation process in incubator. Also in this article we tries to achieve to the greatest efficiency in terms of number of born chickens which was born from eggs and have a system with precise control- finally we survey the average of regression of these three fuzzy parameter.
197
204
Seyed Kamaleddin
Mousavi Mashhadi
Jamal
Ghobadi Dizaj Yekan
Mehdi
Ghasem Nejad Dashtaki
Fuzzy Controller
Heater
Humidity
Oxygen
PID Controller
Temperature.
Article.7.pdf
[
[1]
Lie Wang, Fuzzy Systems and Fuzzy Control, Mohmmad Teshne Lab - Dariush Ofyuni - Nima Safarpour, Khaje Nasir Toosi University Press, (1378)
##[2]
Seyed Mostafa Kia, Fuzzy Logic in MATLAB, Persian Gulf Publishing, (1389)
##[3]
Mehdi Hedayati, applied anatomy and physiology of birds, publishing Mohammadi, (1389)
##[4]
S. V. Kartalopoulos , Fuzzy logic and neural networks (Concepts and Applications), , Shahid Chamran University Press (1381)
##[5]
Jasmin Velagic, Nedim Osmic, Kemal Lutvica, Nihad Kadic, Incubator System Identification and Temperature Control with PLC and HMI, 52nd International Symposium ELMAR-2010, 15-17 September 2010, Zadar, Croatia, (2010), 309-312
##[6]
Wu Shu-ci, Zhou Guo-xiong, Yan Mi-ing, Research of Hybrid Intelligent Control for Incubation, 10th Intl. Conf. on Control, Automation, Robotics and Vision Hanoi, Vietnam, 17–20 December 2008, (2008), 2026-2030
##[7]
M. K. GINALSKI, A. J. NOWAK, L. C. WROBEL , Modelling of heat and mass transfert processes in neonatology, Biomedical Materials, 3 (2008), 1-11
##[8]
K. Lutvica, Temperature control in electric oven, Bachelour tesis, Faculty of Electric Engineering Sarajevo (2009)
]
Bees Algorithm Based Intelligent Backstepping Controller Tuning for Gyro System
Bees Algorithm Based Intelligent Backstepping Controller Tuning for Gyro System
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en
In this paper, an intelligent nonlinear controller is presented by intelligent tuning of the
backstepping method parameters using Bees Algorithm. The proposed controller is utilized to
control of chaos of Gyro system. The backstepping method consists of parameters which could have
positive values. The parameters are usually chosen optional by trial and error method. The
improper selection of the parameters leads to inappropriate responses or even may lead to
instability of the system. The proposed optimal backstepping controller without trial and error
determines the parameters of backstepping controller automatically and intelligently by
minimizing the Integral of Time multiplied Absolute Error (ITAE) and squared controller output.
Finally, the efficiency of the proposed intelligent backstepping controller is illustrated by
implementing the method on the Gyro chaotic system.
205
211
Reza
Gholipour
Alireza
Khosravi
Hamed
Mojallali
Control of chaos
Gyro system
Backstepping method
Bees Algorithm
Article.8.pdf
[
[1]
M. S. Tavazoei, M. Haeri, Chaos control via a simple fractional-order controller, Physics Letters A, 372 (2008), 798-807
##[2]
J. Lu, S. Zhang, Controlling Chen’s chaotic attractor using backstepping design based on parameters identification, Phys Lett A, 286 (2001), 145-149
##[3]
J. H. Park, Synchronization of Genesio chaotic system via backstepping approach, Chaos, Solitons & Fractals, 27 (2006), 1369-1375
##[4]
S. Bowong, F. M. Moukam Kakmeni, Chaos control of uncertain chaotic systems via backstepping approach, ASME J Vibrat Acoust, 128 (2006), 21-27
##[5]
M. T. Yassen, Chaos control of chaotic dynamical systems using backstepping design, Chaos, Solitons & Fractals, 27 (2006), 537-548
##[6]
D. T. Pham, M. Kalyoncu, Optimization of a Fuzzy Logic Controller for a Flexible Single-Link Robot Arm Using the Bees Algorithm, 7th IEEE International Conference on Industrial Informatics , (2009), 475-480
##[7]
D. T. Pham, A. Ghanbarzadeh, E. Koç, S. Otri, S. Rahim, M. Zaidi, The Bees Algorithm – A Novel Tool for Complex Optimisation Problems, Proc. of the 2nd Virtual Int. Conf. on Intelligent Production Machines and Systems (IPROMS 2006), Elsevier (Oxford), (2006), 454-459
##[8]
I. Pan, S. Das, A. Gupta, Tuning of an optimal fuzzy PID controller with stochastic algorithms for networked control systems with random time delay, ISA Transactions, 50 (2011), 28-36
##[9]
H. K. Chen, Chaos and chaos synchronization of a symmetric gyro with linear-plus-cubic damping, Journal of Sound and Vibration, 255 (2002), 719-740
]
Cutting-plane Algorithm for Solving Linear Semi-infinite Programming in Fuzzy Case
Cutting-plane Algorithm for Solving Linear Semi-infinite Programming in Fuzzy Case
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en
This paper introduces a cutting-plane algorithm for solving semi-infinite linear programming problems in fuzzy case; the problem contains a crisp objective linear function and the infinite number of fuzzy linear constraints. In the first step; the designed algorithm solves a LP problem, which was created by the ranking function method based on a fuzzy sub-problem of the original one. In each iteration of the proposed algorithm, a cutting is created by adding a fuzzy constraint of the original problem to the fuzzy sub-problem. The convergence of the algorithm is proved and some numerical examples are given.
212
218
Alireza
Fakharzadeh
Somayeh
Khosravi
Hamidreza
Maleki
Semi-infinite linear programming
Cutting-plane
Fuzzy linear programming.
Article.9.pdf
[
[1]
R. E. Bellman, L. A. Zadeh, Decision making in a fuzzy environment, Management Science, 17 (1970), 141-164
##[2]
B. Betro, An accelerated centeral cutting plane, Math. Program, 101 (2004), 479-495
##[3]
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The Bankruptcy Prediction in Tehran Share Holding Using Neural Network and Its Comparison with Logistic Regression
The Bankruptcy Prediction in Tehran Share Holding Using Neural Network and Its Comparison with Logistic Regression
en
en
The use of financial ratios for predicting companies' bankruptcy has always been considered by universities and economical institutions especially banks and other financial organizations. In such studies, statistical models like multiple distinctive analyses (MDA), logit Analysis, probit Analysis have usually been used. In this study, the prediction of accepted productive companies' bankruptcy in Tehran negotiable papers exchange has been paid by the use of artificial neural network (ANN) model and we have also made a comprehensive review on the models of bankruptcy prediction. In this study, artificial neural network model with logistic regression (LR) statistical model that is a useful statistical model in bankruptcy prediction has been compared. Our findings from these models on the basis of 80 companies' data showed that artificial neural network model has more accuracy than logistic regression statistical model in bankruptcy prediction.
219
228
Mahnaz
Bagheri
Mehrdad
Valipour
Vahid
Amin
Bankruptcy prediction
financial ratios
Logistic Regression (LR)
Artificial Neural Network (ANN).
Article.10.pdf
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]
A Survey of Hierarchical Clustering Algorithms
A Survey of Hierarchical Clustering Algorithms
en
en
Clustering algorithms classify data points into meaningful groups based on their similarity to
exploit useful information from data points. They can be divided into categories: Sequential
algorithms, Hierarchical clustering algorithms, Clustering algorithms based on cost function
optimization and others. In this paper, we discuss some hierarchical clustering algorithms and their
attributes, and then compare them with each other.
229
240
Marjan Kuchaki
Rafsanjani
Zahra Asghari
Varzaneh
Nasibeh Emami
Chukanlo
Clustering
Hierarchical clustering algorithms
Complexity.
Article.11.pdf
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