]>
2014
11
4
90
A New Analytical Method for Solving Hamilton-jacobi-bellman Equation
A New Analytical Method for Solving Hamilton-jacobi-bellman Equation
en
en
In this paper, we apply a modification of variational iteration method using He's polynomials for a class of nonlinear optimal control problems which are converted to the Hamilton-Jacobi-Bellman equations (HJB) and present a convergence theorem of the method. The proposed modification is made by introducing He's polynomials in the correction functional. The suggested algorithm is quite efficient and is practically well suited for using in these problems. Some examples are given to demonstrate the simplicity and efficiency of the proposed method.
252
263
M.
Matinfar
M.
Saeidy
Optimal control problem
Homotopy perturbation method
Variational iteration method
Numerical solution.
Article.1.pdf
[
[1]
M. Itik, M. U. Salamci, S. P. Banksa, Optimal control of drug therapy in cancer treatment, Nonlinear Analysis, 71(12) (2009), 1473-1486
##[2]
W. L. Garrard, J. M. Jordan , Design of nonlinear automatic flight control systems , Automatica, 13(5) (1977), 497-505
##[3]
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)
##[4]
O. Stryk, R. Bulirsch, Direct and indirect methods for trajectory optimization, Annals of Operations Research, 37(1-4) (1992), 357-373
##[5]
C. J. Goh, K. L. Teo, Control parameterization: a unified approach to optimal control problem with general constraints, Automatica, 24(1) (1988), 3-18
##[6]
R. Bellman, On the theory of dynamic programming, Proceedings of the National Academy of Sciences, USA, 38(8) (1952), 716-719
##[7]
L. S. Pontryagin, Optimal control processes, Uspekhi Matematicheskikh Nauk, 14 (1959), 3-20
##[8]
R. W. Beard, G. N. Saridis, J. T. Wen, Galerkin approximations of the generalized Hamilton- Jacobi-Bellman equation, Automatica, 33(12) (1997), 2159-2177
##[9]
G. Y. Tang, Suboptimal control for nonlinear systems: a successive approximation approach, System and Control Letters, 54(5) (2005), 429-434
##[10]
G. Y. Tang, H. P. Qu, Y. M. Gao, Sensitivity approach of suboptimal control for a class of nonlinear systems, Journal of Ocean University of Qingdao, 32(4) (2002), 615-620
##[11]
M. A. Abdou, A. A. Soliman, Variational iteration method for solving Burger's and coupled Burger's equations, J. Comput. Appl. Math., 181 (2005), 245-251
##[12]
M. R. Caputo, Foundations of dynamic economic analysis: optimal control theory and applications, Cambridge University press, (2005)
##[13]
A. Ghorbani, Beyond Adomian polynomials: He polynomials, Chaos Solitons Fractals, 39 (2009), 1486-1492
##[14]
A. Ghorbani, J. Saberi-Nadjafi, He's homotopy perturbation method for calculating adomian polynomials, Int. J. oF Non. Sci. and Num. Simul., 8 (2007), 229-232
##[15]
J. H. He, Homotopy perturbation technique, Comput. Math. in Appl. Mech. and Eng., 178(3-4) (1999), 257-62
##[16]
J. H. He, Variational iteration method:Some recent results and new interpretations, J. Comput. Appl. Math., 207 (2007), 3-7
##[17]
M. Inokuti, et al., General use of the Lagrange multiplier in non-linear mathematical physics, in: S. Nemat-Nasser (Ed.), Variational Method in the Mechanics of Solids, Pergamon Press, Oxford, (1978), 156-162
##[18]
S. T. Mohyud-Din, M. A. Noor, KI. Noor, Variational Iteration Method for Burgers' and Coupled Burgers' Equations Using He's Polynomials, Zeitschrift Fur Naturforschunge Section A-A J. oF Phy. Sci., 65 (2010), 263-267
##[19]
M. Matinfar, M. Saeidy, The Homotopy perturbation method for solving higher dimensional initial boundary value problems of variable coefficients, W. J. of Model. and Simu., 5 (2009), 75-83
##[20]
A. M. Wazwaz , The variational iteration method: A reliable analytic tool for solving linear and nonlinear wave equations, Comput. and Math. with Appl., 54 (2007), 926-932
##[21]
A. M. Wazwaz, The variational iteration method for rational solutions for KdV, K(2,2), Burgers, and cubic Boussinesq equations, J. Comput. Appl. Math., 207 (2007), 18-23
##[22]
S. A. Yousefi, M. Dehghan, A. Lotfi, Finding the optimal control of linear systems via He's variational iteration method, International Journal of Computer Mathematics, (2010), 1042-1050
]
Control of Puma-560 Robot Using Feedback Linearization Control Method and Kalman Filter Estimator for Regulation and Tracking Purpose
Control of Puma-560 Robot Using Feedback Linearization Control Method and Kalman Filter Estimator for Regulation and Tracking Purpose
en
en
This research is presented to control a PUMA 560 robot which is well-known industrial robot with six degrees of freedom. It is a RRRRRR robot type which can do various tasks such as point welding in automotive industry and similar industries. The mathematical model is derived from dynamical equations by the means of Euler-Lagrange method. Stochastic feedback linearization with Kalman filter controller is implemented to control the PUMA 560 robot end effector.The regulation and tracking results are represented. The controller is examined in normal situation and even in presence of disturbances. Finally, the results indicate good performance of the controller.
264
276
Ehsan
Zakeri
Seyed Alireza
Moezi
Mehdi
Zare
Mostafa Parnian
Rad
Feedback linearization
Kalman filter
PUMA 560
Nonlinear system.
Article.2.pdf
[
[1]
J. Mozaryn, J. E. Kurek, Design of decoupled sliding mode control for the PUMA 560 robot manipulator, Robot Motion and Control, 2002. RoMoCo '02. Proceedings of the Third International Workshop on, 45 ( 2002), 9-11
##[2]
A. K. Bejczy, T.-J. Tarn, X. Yun, S. Han, Nonlinear feedback control of PUMA 560 robot arm by computer, Decision and Control, 1985 24th IEEE Conference on , 24 (1985), 1-1680
##[3]
A. Vahidian Kamyad, M. Shokohi Nia, M. R. Shokohi Nia, the Estimation of Transference Rate HIV Infection into AIDS and Mortality in Children by Fuzzy Control, , 2 (2011), 241-254
##[4]
S. A. Moezi, E. Zakeri, Y. Bazargan-Lari, M. Tavallaeinejad, Control of a Ball on Sphere System with Adaptive Neural Network Method for Regulation Purpose, Journal of Applied Sciences , 14 (17) (2014), 1984-1989
##[5]
H. R. Erfanian, M. H. Noori Skandari, Optimal Control of an HIV Model, The Journal of Mathematics and Computer Science, 2 (), 241-254
##[6]
Ehsan Zakeri, Yousef Bazargan-Lari, Mohammad Eghtesad, Simultaneous Control of GMAW Process and SCARA Robot in Tracking a Circular Path via a Cascade Approach, Trends in Applied Sciences Research, 7 (2012), 845-858
##[7]
M. R. Dastranj, M. Moghaddas, K. Esmaeili Khoshmardan, A. Zare , Robust Control of Inverted Pendulum Using Fuzzy Sliding Mode Control (FSMC), The Journal of Mathematics and Computer Science, 2 (2011), 659-666
##[8]
E. Sareban, A. Aminian, S. K. Mousavi Mashhadi, Synthesis Water Level Control by Fuzzy Logic, The Journal of Mathematics and Computer Science, 9 (2014), 300-313
##[9]
K. Ch. Chiou, S. J. Huang, An adaptive fuzzy controller for robot manipulators, Mechatronics, 15 (2005), 151-177
##[10]
Y. Tang, F. Sun, Z. Sun, Neural Network Control of Flexible-link Manipulators Using Sliding Mode, neuro computing, 70 (2006), 288-295
##[11]
L. Tian, C. Collins, Adaptive Neuro-Fuzzy Control of a Flexible Manipulator, Mechatronics, 15 (2005), 1305-1320
##[12]
M. W. Spong, On the robust control of robot manipulators, IEEE Trans. On Automatic Control, 37 (1992), 1782-1786
##[13]
E. Zakeri, S. A. Moezi, Y. Bazargan-Lari, Control of a Ball on Sphere System with Adaptive Feedback Linearization method for regulation purpose, MAJLESI JOURNAL OF MECHATRONIC SYSTEMS, 2(3) (2013)
##[14]
Y. Bazargan-Lari, E. Zakeri, A. R. Ghahramani, K. Bazargan-Lari, S. A. Moezi, Disturbance Rejection Control of 3-D Overhead Gantry Crane System for Regulation purpose, National Conference on Mechanical Engineering February 22-23, 2012, Islamic Azad University, Shiraz Branch, Shiraz, , Iran (2012)
]
Anr an Algorithm to Recommend Initial Cluster Centers for K-means Algorithm
Anr an Algorithm to Recommend Initial Cluster Centers for K-means Algorithm
en
en
Clustering is one of the widely used knowledge discovery techniques to detect structure of datasets and can be extremely useful to the analyst. In center based clustering algorithms such as k-means, choosing initial cluster centers is really important as it has an important impact on the clustering result. It is desirable to select initial centers which are well separated. In this paper, we have proposed an algorithm to find initial cluster centers based on choosing two attributes that can describe the data space better and using the number of neighbors in a specific radius in data space. The proposed Attribute and Neighborhood Radius based (ANR) initial cluster center computing algorithm is applied to several well-known datasets .experimental results shows that it prevents form choosing noise data points as cluster center and tries to choose data points from dense areas in data space.
277
290
Arash Ghorbannia
Delavar
Gholam Hasan
Mohebpour
Data mining
clustering
k-means
Initial cluster centers
Article.3.pdf
[
[1]
P. S. Bradley, U. M. Fayyad, Refining initial points for k-means clustering, In Proceedings of the Fifteenth International Conference on Machine learning (ICML‘98) , (1998), 91-99
##[2]
A. Likas, N. A. Vlassis, J. J. Verbeek, The global k-means clustering algorithm, Pattern Recognition , 36, 2 (2003), 451-461
##[3]
S. S. Khan, A. Ahmad, Cluster center initialization algorithm for k-means clustering, Pattern Recognition Letters 25, 11 (2004), 1293-1302
##[4]
S. Deelers, S. Auwatanamongkol, k-means algorithm with initial cluster centers derived from data partitioning along the data axis with the highest variance, International Journal of Computer Science , 2 (2007), 247-252
##[5]
K. Arai, A. R. Barakbah, Hierarchical k-means: an algorithm for centroids initialization for k-means, Reports of the Faculty of Science and Engineering Saga University , 36, 1 (2007), 25-31
##[6]
F. Cao, J. Liang, G. Jiang, An initialization method for the k-means algorithm using neighborhood model, Computers & Mathematics with Applications, 58, 3 (2009), 474-483
##[7]
Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Norwell, MA, USA (1992)
##[8]
A. H. Ahmed, W. Ashour, An initialization method for the k-means algorithm using rnn and coupling degree, International Journal of Computer Applications, Published by Foundation of Computer Science, New York, USA. , 25, 1 (2011), 1-6
##[9]
M. F. Eltibi, W. M. Ashour, Initializing k-means clustering algorithm using statistical information, International Journal of Computer Applications, Published by Foundation of Computer Science,New York, USA. , 29, 7 (2011), 51-55
##[10]
M. E. Celebi, H. A. Kingravi, P. A. Vela, A comparative study of efficient initialization methods for the k-means clustering algorithm, Expert Systems with Applications , 40, 1 (2013), 200-210
##[11]
Liang Bai, Jiye Liang, Chao Sui, Chuangyin Dang , Fast global k-means clustering based on local geometrical information , Information Sciences , 245 (2013), 168-180
##[12]
M. Erisoglu, N. Calis, S. Sakallioglu, A new algorithm for initial cluster centers in k-means algorithm, Pattern Recogn. Lett., 32, 14 (2011), 1701-1705
##[13]
, , http://repository.seasr.org/Datasets/UCI/arff/ , ()
##[14]
R. Maghsoudi, A. Ghorbannia Delavar, S. Hoseyny, R. Asgari, Y. Heidari, Representing the New Model for Improving K-Means Clustering Algorithm based on Genetic Algorithm, The Journal of Mathematics and Computer Science , 2 (2011), 329-336
]
Designing and Implementing a Distributed Genetic Algorithm for Optimizing Work Modes in Wireless Sensor Network
Designing and Implementing a Distributed Genetic Algorithm for Optimizing Work Modes in Wireless Sensor Network
en
en
In this paper it is tried to present a solution for optimizing energy consumption in the sensors of wireless network by using distributed genetic algorithm and solving the famous problem of graph coloration. this idea formed by modeling sensors of wireless network by the help of graph and posing the problems of graph coloration with the description of work groups in scheduling nodes in wireless sensor networks. In this way we can save energy and conduct quality services in different time and place of wireless sensor network by determining some work groups in different time and different network tree node.
291
299
Mehdi
Eslami
Javad
Vahidi
Majid
Askarzadeh
Genetic Algorithm
Wireless Sensor Network
Graph Coloration
Optimizing Energy Consumption
Article.4.pdf
[
[1]
A. C. M. Ran, M. C. B. Reurings, A fixed point theorem in partially ordered sets and some applications to matrix equations, Proc. Amer. Math. Soc., 132 (2003), 1435-1443
##[2]
I. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, A Survey on Sensor Networks, IEEE Communications Magazine, 40 (2002), 102-116
##[3]
G. J. Chaitin, Register Allocation and splitting via graph coloring , Proc. Of ACM SIGPLAN 82 Symposium on Compiler Construction, (1987), 98-105
##[4]
D. S. Jobnson, C. R. Aragon, L. A. McGoach, C. Schevon, Optimization by simulated annealing: an exprimental evaluation, Part 2, graph coloring and number partitioing, Operations Research, 39(3) (1991), 378-406
##[5]
David E. Golldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesly Pub., (1989)
##[6]
Su-Young Parka, Jung Hyun Choia, Sookyun Wangb, Seok Soon Parka, Design of a water quality monitoring network in a large river system using the genetic, algorithm, Ecological Modelling , 1 9 9 (2006), 289-297
##[7]
W. Heinzelman, A. Chandrakasan, H. Balakrishnan, Energy-Efficient Communication Protocol for Wireless Microsensor Networks, In Proceedings of the 33rd Hawaii International Conference on System Sciences (HICSS '00), (2000)
##[8]
I. Sim, K. Jin Choi, K. Kwon, Jaiyong Lee, Energy Efficient Cluster header Selection Algorithm in WSN, In Proceedings of IEEE International Conference on Complex, Intelligent and Software Intensive Systems, (2009), 584-587
##[9]
D. Riordan Gupta, S. Sampalli, Cluster-head Election using Fuzzy Logic for Wireless Sensor Networks, In Proceedings of IEEE Communication Networks and Services Research Conference, (2005), 255-260
]
A New Method for Searching Keyword in Cloud Servers Using Anfis
A New Method for Searching Keyword in Cloud Servers Using Anfis
en
en
With the popularity of computing cloud in recent decade, sensitive information is stored in cloud systems. To protect the sensitive data before saving, cryptographic operation must be done. In cryptography with the traditional method the user can search its data with high security ability, but the disadvantage of this method is to enter the same items in data for searching. And there is not any virtual error for false type. This major weakness causes not to harmonize the alone mentioned method in searching on cloud servers. In this paper we solve the search problem of cloud encrypted words in light and fuzzy system with maintaining the data security. Its aim is to search in cases which there is not the user's possible errors be changed into the rules using a light and fuzzy network, and discussed with the use of neural network to learning the pattern of the server data, and when searching the network provides the closest option to the user. Our experimental results show that this method of diagnosis is above 90 percent in the scope of the written rules for fuzzy system.
300
308
Fatemeh
Goli
Hossein
Momeni
Ali
Yavari
cloud computing
fuzzy search
neural search
neuro-fuzzy
cryptography
Article.5.pdf
[
[1]
M. Bellare, A. Boldyreva, A. O’Neill, Deterministic and efficiently searchable encryption, in Proceedings of Crypto 2007, volume 4622 ofLNCS, Springer-Verlag (2007)
##[2]
F. Bao, R. Deng, X. Ding, Y. Yang, Private query on encrypted data in multi-user settings, in Proc. of ISPEC’08, (2008)
##[3]
R. Curtmola, J. A. Garay, S. Kamara, R. Ostrovsky, Searchable symmetric encryption: improved definitions and efficient constructions, in Proc. of ACM CCS’06, (2006)
##[4]
Jin Li, Qian Wang, Cong Wang, Ning Cao, Kui Ren, Wenjing Lou, Fuzzy Keyword Search over Encrypted Data in Cloud Computing, , (2010)
##[5]
V. Levenshtein, Binary codes capable of correcting spurious insertions and deletions of ones, Problems of Information Transmission, 1 (1965), 8-17
##[6]
B. Waters, D. Balfanz, G. Durfee, D. Smetters, Building an encrypted and searchable audit log, in Proc. of 11th Annual Network and Distributed System, (2004)
##[7]
J. M. Zurada, Introduction to artificial neural systems , vol. 8: West publishing company , New York (1992)
##[8]
S. Mitra, Y. Hayashi, Neuro-fuzzy rule generation: survey in soft computing framework, Neural Networks, IEEE Transactions on, 11 (2000), 748-768
##[9]
C. Li, J. Lu, Y. Lu, Efficient merging and filtering algorithms for approximate string searches, in Proc. of ICDE’08, (2008)
##[10]
S. Ji, G. Li, C. Li, J. Feng, Efficient interactive fuzzy keyword search, in Proc. of WWW’09, (2009)
##[11]
D. Song, D. Wagner, A. Perrig, Practical techniques for searches on encrypted data, in Proc. of IEEE Symposium on Security andPrivacy’00, (2000)
##[12]
E.-J. Goh, Secure indexes, Cryptology ePrint Archive, Report2003/216 (2003)
##[13]
Y.-C. Chang, M. Mitzenmacher, Privacy preserving keyword searches on remote encrypted data, in Proc. of ACNS’05, (2005)
##[14]
D. Boneh, G. D. Crescenzo, R. Ostrovsky, G. Persiano, Public key encryption with keyword search, in Proc. of EUROCRYP’04, (2004)
##[15]
J. Feigenbaum, Y. Ishai, T. Malkin, K. Nissim, M. Strauss, R. N.Wright, Secure multiparty computation of approximations, in Proc. Of ICALP’01. , ()
##[16]
R. Ostrovsky, Software protection and simulations on oblivious rams, Ph.D dissertation, Massachusetts Institute of Technology (1992)
]
Solution of Impulsive Differential Equations with Boundary Conditions in Terms of Integral Equations
Solution of Impulsive Differential Equations with Boundary Conditions in Terms of Integral Equations
en
en
In this article, we indroduce solution of impulsive differential equations with boundary
conditions by using vareational interation method (VIM) in terms of integral equations. For
finding the above solution, at first we obtian a solve for differential equations with boundary
conditions.
309
318
Mohsen
Rabbani
Impulsive
Differential
Equation
Integral.
Article.6.pdf
[
[1]
M. A. Abdou, A. A. Soliman, Variational iteration method for solving Burger's and coupled Burger's equations, J.comput.Appl.Math, 181 (2005), 245-251
##[2]
J. Biazar, H. Ghazvini, he's variational iteration method for solving linear and nonlinear systems of ordinary differential equations, Applied mathematics and computation, 191 (2007), 287-297
##[3]
M. Dehghan, M. Tatari, the use of He's variational iteration method for solving the Fokker- Planck equation, Phys.scripta, 74 (2006), 310-316
##[4]
D. Guo, X. Liu, Extremal solutions of nonlinear impulsive integro-differential equations in Banach Spaces, J. Math. Appl., 177 (1993), 538-552
##[5]
J. H. He, variational iteration method for nonlinear and it's applications, Mechanics and practice, 20, (1) (1998), 30-32
##[6]
J. H. He, variational iteration method - a kind of nonlinear analytical technique:Some examples, Int.Journal of Nonlinear Mechanics, 34 (1999), 699-708
##[7]
M. Inokuti, general use of the Lagrange multiplier in in nonlinear mathematical physics, in: S.Nemat-nasser(Ed.), Variational Method in Mechanics of solids, Progamon press, oxford, (1978), 156-162
##[8]
X. Liu, Monotone iterative technique for impulsive differential equations in a Banach space, J. Math. Phy. Sci. , 24 (1990), 183-191
]
An Iterative Method for Semigroups of Nonexpansive Mappings
An Iterative Method for Semigroups of Nonexpansive Mappings
en
en
We introduce an iterative method for finding a common fixed point of a semigroup of infinite family of
nonexpansive mappings in Hilbert space, with respect to a sequence of left regular means defined on an
appropriate space of bounded real valued functions of the semigroup. we prove the strong convergence of
the proposed iterative algorithm to the unique solution of a variational inequality, which is the optimality
condition for a minimization problem.
319
329
A.
Dianatifar
F.
Golkar
A. M.
Forouzanfar
Hilbert space
Amenable semigroups
Common fixed point
Nonexpansive mappings.
Article.7.pdf
[
[1]
A. T. Lau, N. Shioji, W. Takahashi, Existences of nonexpansive retractions for amenable semigroups of nonexpansive mappings and nonlinear ergodic theorems in Banach spaces, J. Funct. Anual. , 161 (1999), 62-75
##[2]
G. Marino, H. K. Xu, A general iterative method for nonexpansive mappings in Hilbert spaces, j. Math. Anal. Appl., 318 (2006), 43-52
##[3]
H. K. Xu, Viscosity approximation methods for nonexpansive mappings, J. Math. Anal. Appl., 298 (2004), 279-291
##[4]
H. Piri, H. Vaezi, Strong convergence of a generalized iterative method for semigroups of nonexpansive mappings in Hilbert spaces, Fixed Point Theory Appl. , doi:10. 1155/2010/907275. (2010)
##[5]
K. Shimoji, W. Takahashi , Strong convergence to common fixed points of infinite nonexpansive mappings and applications, Taiwanese J. Math., 5 (2001), 387-404
##[6]
R. E. Bruck, On the convex approximation property and the asymptotic behavior of nonlinear contractions in Banach spaces, Israel J. Math. , 38 (1981), 304-314
##[7]
Sh. Banerjee, B. S. Choudhury, Weak and strong convergence theorems of a new iterative process with errors for common fixed points of a finite families of asymptotically nonexpansive mappings in the intermediate sense in Banach spaces, TJMCS, 11 (2014), 79-85
##[8]
S. S. Zhang, J. H. W. Lee, C. K. Chan, Algorithms of common solutions to quasi variational inclusion and fixed point problems, Appl. Math. Mech., 29 (2008), 571-581
##[9]
T. Suzuki, Strong convergence of Krasnoselskii and Mann’s type sequences for oneparameter nonexpansive semigroups without Bochner integrals, J. Math. Anal. Appl. , 305 (2005), 227-239
##[10]
W. Takahashi, A nonlinera ergodic theorem for an amenable semigroup of nonexpansive mappings in Hilbert space, Proc. Amer. Math. Soc. , 81 (1981), 253-256
##[11]
W. Takahashi, Nonlinear Functional Analysis, Yokohama Publishers, Yokohama (2000)
##[12]
Y. Yao, A general iterative method for a finite family of nonexpansive mappings, Nonlinear Anal. , 66 (2007), 2676-2687
]
Tabsum- A New Persian Text Summarizer
Tabsum- A New Persian Text Summarizer
en
en
With the rapid increase in the amount of online text information, it became more important to have tools that would help users distinguish the important content. Automatic text summarization attempts to address this problem by taking an input text and extracting the most important content of it. However, the determination of the salience of information in the text depends on different factors and remains as a key problem of automatic text summarization. In the literature, there are some studies that use lexical chains as an indicator of lexical cohesion in the text and as an intermediate representation for text summarization. Also, some studies make use of genetic algorithms in order to examine some manually generated summaries and learn the patterns in the text which lead to the summaries by identifying relevant features which are most correlated with human generated summaries. In this study, we combine these two approaches of summarization. Firstly, some of preprocessing operations like normalizer, tokenizer, stop word remover, stemmer, and POS tagger are done on the text. After that for each sentence we have only semantic words that are independent. Then, by set of position, thematic, and coherence features we score sentences. The final score of each sentence will be the integration of those features. Each feature has its own weight and should be identified to have well summary. For this reason first system goes throw learning phase to determine ache feature weight by genetic algorithm. The next phase is testing phase. In this phase system receives new documents and uses Persian WordNet and lexical chains to extract deep level of knowledge about the text. This knowledge is combined with other higher level analysis results. Finally, sentences are scored, sorted, and selected and summary is made. We evaluated our proposed system by two methods. 1) Precision/recall, 2) TabEval (a new evaluation tool for Persian text summarizers). We compared our system with two other Persian summarizers (FarsiSum, Ijaz). Results showed that our system had higher performance rather than others (i.e. higher precision/recall average and the best average score of TabEval).
330
342
Saeid
Masoumi
Mohammad-Reza
Feizi-derakhshi
Raziyeh
Tabatabaei
Summarization
Text Summarizer
Mono-Document Summarization
Extractive Summarization
Persian Text Summarization.
Article.8.pdf
[
[1]
A. Kiani, M. R. Akbarzadeh, Automatic Text Summarization Using: Hybrid Fuzzy GA-GP, In IEEE International Conference on Fuzzy Systems, (2006)
##[2]
I. Mani, Automatic Summarization, John Benjamins Publishing Company, Amsterdam/Philadelphia (2001)
##[3]
Inderjcet Main, the MITRE corporation 11493 Sanset Hills noad , , USA (2003)
##[4]
N. Mazdak, FarsiSum-a persian text summarizer, Master thesis,Department of linguistics, Stockholm University. (2004)
##[5]
H. Dalianis, SweSum-A Text Summarizer for Swedish, Technical report, TRITANA-P0015, IPLab-174. (2000)
##[6]
M. Shamsfard, T. Akhavan, M. E. Joorabchi, Persian Document Summarization by Parsumist, World Applied Sciences Journal 7 (Special Issue of Computer & IT), (2009), 199-205
##[7]
F. Kiyomarsi, F. R. Esfahani, Optimizing Persian Text Summarization Based on Fuzzy Logic Approach, International Conference on Intelligent Building and Management. , (2011)
##[8]
Z. Karimi, M. Shamsfard, Summarization of Persian texts, In Proceedings of 11th International CSI computer Conference, Tehran, Iran. (2006)
##[9]
M. A. Honarpisheh, G. Ghasem-sani, G. Mirroshandel, A Multi-Document Multi-Lingual Automatic Summarization System, Proceedings of the 3rd Joint Conference on Natural Language Processing, (2008), 733-738
##[10]
G. F. D. Jong, An overview of the FRUMP system, W. G. Lehnert and M. H. Ringle (Editors), Strategies for Natural Language Processing, Erlbaum, Hillsdale, NJ (1982)
##[11]
U. Hahn, I. Mani, Automatic Text Summarization: Methods, Systems, and Evaluations, In International Joint Conference on Artificial Intelligence (IJCAI), (1998)
##[12]
E. Hovy, C. Y. Lin, Automated Text Summarization in SUMMARIST, I. Mani and M. T. Maybury (Editors), Advances in Automatic Text Summarization, , The MIT Press, Cambridge, MA, (1999), 81-94
]