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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

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With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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  2/2010 - 13

Genetic Algorithm based Servo System Parameter Estimation during Transients

REZAZADEH, A. See more information about REZAZADEH, A. on SCOPUS See more information about REZAZADEH, A. on IEEExplore See more information about REZAZADEH, A. on Web of Science
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Download PDF pdficon (1,703 KB) | Citation | Downloads: 1,183 | Views: 3,433

Author keywords
parameter estimation, transient response, genetic optimization, system identification, servo drive

References keywords
rezazade(4), optimization(4), genetic(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2010-05-31
Volume 10, Issue 2, Year 2010, On page(s): 77 - 81
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2010.02013
Web of Science Accession Number: 000280312600013
SCOPUS ID: 77954628271

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The application of Genetic Optimization Algorithm in estimation of the parameters of servo electrical drives is proposed. In comparison with this planned method, least squared error (LSE) estimation method is considered as an expedient method for parameter estimation. Regardless of LSE estimation, Genetic Algorithm method is not restricted to the linear systems with respect to the parameters. GA is imported as an optimization method in comparison with conventional optimization methods because of its power in searching whole solution space with more probability to finding the global optimum. As a condition for convergence, transient excitation is considered instead of persistent excitation. Finally, comparison between LSE and GA based parameter estimation is presented to indicate robustness and resolution of GA identification method. It will be shown that the GA method of estimation has better results in the startup and transients of the system where there is a lack of persistent excitation.

References | Cited By  «-- Click to see who has cited this paper

[1] Torsten Soderstorm and Petre Stoica, "System Identification", Prentice Hall International Series in Systems and Control Engineering, 1989. [PermaLink]

[2] Bor-Sen Chen, Bore-Kuen Lee and Sen-Chueh Peng "Maximum Likelihood Parameter Estimation of F-ARIMA Processes Using the Genetic Algorithm in the Frequency Domain", IEEE Transactions on Signal Processing, Vol. 50, No. 9, September 2002.
[CrossRef] [Web of Science Times Cited 19] [SCOPUS Times Cited 23]

[3] Sunil L. Kukreja, Robert E. Kearney and Henrietta L. Galiana, "A Least-Squares Parameter Estimation Algorithm for Switched Hammerstein Systems with Applications to the VOR", IEEE Transactions on Biomedical Engineering, Vol. 52, No. 3, March 2005.
[CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 38]

[4] Benjamin C. Kuo, "Automatic Control Systems" (sixth edition), Prentice Hall, 1991. [PermaLink]

[5] J. C. Lagarias, J. A. Reeds, M. H. Wright and P. E. Wright, "Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions", SIAM Journal of Optimization, Vol. 9, Number 1, pp. 112-147, 1998.
[CrossRef] [Web of Science Times Cited 3288] [SCOPUS Times Cited 3829]

[6] Ali Reza Rezazade, Arash Sayyah, Mitra Aflaki, "Optimization of THD and Suppressing Certain Order Harmonics in PWM Inverters using Genetic Algorithms", IEEE International Symposium on Intelligent Control (ISIC) 4-6 October 2006, in Munich, Germany.

[7] Arash Sayyah, Mitra Aflaki, Ali Reza Rezazade, "GA-Based Optimization of Total Harmonic Current Distortion and Suppression of Chosen Harmonics in Induction Motors", SPEEDAM Symposium, 23-26 May 2006, Taormina (Italy).
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 4]

[8] Ali Reza Rezazade, Arash Sayyah, Mitra Aflaki, "Optimal PWM for Minimization of Total Harmonic Current Distortion in High-Power Induction Motors using Genetic Algorithms", SICE - ICASE International Joint Conference 2006 EXCO (Busan Exhibition & Convention Center), Busan, KOREA.
[CrossRef] [SCOPUS Times Cited 11]

[9] A. R. Rezazade, M. Lankarani, "Parameter Estimation Optimization Based on Genetic Algorithm Applied to DC Motor", Proceedings of ICEE 2007 International Conference on Electrical Engineering, Lahore, Pakistan, 11-12 April 2007.
[CrossRef] [SCOPUS Times Cited 10]

References Weight

Web of Science® Citations for all references: 3,333 TCR
SCOPUS® Citations for all references: 3,915 TCR

Web of Science® Average Citations per reference: 370 ACR
SCOPUS® Average Citations per reference: 435 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2018-07-19 11:17 in 52 seconds.

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania

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