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JCR Impact Factor: 0.595
JCR 5-Year IF: 0.661
Issues per year: 4
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Next issue: Nov 2017
Avg review time: 75 days


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

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


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ABC Algorithm based Fuzzy Modeling of Optical Glucose Detection, SARACOGLU, O. G., BAGIS, A., KONAR, M., TABARU, T. E.
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LATEST NEWS

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Apr-04
We have the confirmation Advances in Electrical and Computer Engineering will be included in the EBSCO database.

2017-Feb-16
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.

2017-Jan-30
We have the confirmation Advances in Electrical and Computer Engineering will be included in the Gale database.

2016-Dec-17
IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 31st of March 2017, placing "IoT - " before the paper title in OpenConf.

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  1/2010 - 17

Elite Based Multiobjective Genetic Programming in Nonlinear Systems Identification

PATELLI, A. See more information about PATELLI, A. on SCOPUS See more information about PATELLI, A. on IEEExplore See more information about PATELLI, A. on Web of Science, FERARIU, L. See more information about FERARIU, L. on SCOPUS See more information about FERARIU, L. on SCOPUS See more information about FERARIU, L. on Web of Science
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (427 KB) | Citation | Downloads: 985 | Views: 3,162

Author keywords
evolutionary algorithms, genetic programming, multiobjective optimization, nonlinear system identification

References keywords
programming(7), genetic(6), evolutionary(6), systems(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2010-02-27
Volume 10, Issue 1, Year 2010, On page(s): 94 - 99
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2010.01017
Web of Science Accession Number: 000275458900017
SCOPUS ID: 77954683610

Abstract
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The nonlinear systems identification method described in the paper is based on genetic programming, a robust tool, able to ensure the simultaneous selection of model structure and parameters. The assessment of potential solutions is done via a multiobjective approach, making use of both accuracy and parsimony criteria, in order to encourage the selection of accurate and compact models, characterized by expected good generalization capabilities. The evolutionary process is implemented from an elitist standpoint, and upgraded by means of two original contributions, namely an adaptive niching mechanism and an elite clustering procedure. The authors have also suggested a set of enhancements to aid the genetic operators in effectively exploring the space of possible model structures. In symbiosis with the customized genetic operators, a QR local optimization procedure was integrated within the algorithm. It exploits the nonlinear, linear in parameter form that the working models are generated in, for providing a faster parameter computation. The performances of the proposed methodology were revealed on two applications, of different complexity levels: the identification of a simulated nonlinear system and the identification of an industrial plant.


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

[1] H. Wey, S. A. Billings, J. Lui, "Term and Variable Selection for Nonlinear Models", Int. J. Control 77, pp. 86-110, 2004

[2] N. Nedjah, A. Abraham, L. de Macedo Mourelle, "Genetic systems programming : theory and experiences", Springer, Netherlands, 2006 [PermaLink]

[3] P. J. Flemming, R. C. Purshouse, "Evolutionary Algorithms in Control Systems Engineering: A Survey", Control Engineering Practice 10, pp. 1223-1241, 2002
[CrossRef] [Web of Science Times Cited 252] [SCOPUS Times Cited 360]


[4] J. R. Koza, "Genetic Programming: On the Programming of Computers by Means of Natural Selection", Cambridge, MA, MIT Press, 1992, pp. 73-190 [PermaLink]

[5] J. Madar, J. Abonyi, F. Szeifert, "Genetic Programming for System Identification", 2005, Available: http://www.fmt.vein.hu/softcomp/isda04_gpolsnew.pdf

[6] R. Riolo, T. Soule, B. Worzel, "Genetic Programming Theory and Practice IV", Springer, USA, 2007
[CrossRef]


[7] K. Rodriguez-Vasquez, C. M. Fonseca, P. J. Flemming, "Identifying the Structure of Nonlinear Dynamic Systems Using Multiobjective Genetic Programming", IEEE Transactions on Systems Man and Cybernetics, Part A - Systems and Humans, 34, pp. 531-534, 2004
[CrossRef] [Web of Science Times Cited 56] [SCOPUS Times Cited 74]


[8] J. Knowles , D. Corne, K. Deb, "Multiobjective Problem Solving from Nature - From Concepts to Applications", Natural Computing Series, Springer, USA, 2008 [PermaLink]

[9] K. Deb, "Multiobjective Optimization using Evolutionary Algorithms", John Wiley and Sons, USA, 2001 [PermaLink]

[10] Y. G. Woldesenbet, G. C. Yen, "Dynamic Evolutionary Algorithm with Variable Relocation", IEEE Transactions on Evolutionary Computation, vol. 13, no. 3, pp. 500-513, 2009
[CrossRef] [Web of Science Times Cited 38] [SCOPUS Times Cited 49]


[11] L. Ferariu, A. Patelli, "Multi-objective Genetic Programming for Nonlinear System Identification", Proc. of ICANNGA09, Kuopio, Finland, 2009

[12] T. Back, D. Fogel, Z. Michalewicz, "Evolutionary Computation - Advanced Algorithms and Operators", Institute of Physics Publishing, 2000 [PermaLink]

[13] L. Ferariu, M. Voicu, "Nonlinear System Identification Based on Evolutionary Dynamic Neural Networks wih Hybrid Structure", Proc. of IFAC Congress, Prague, Czech Republic, 2005

References Weight

Web of Science® Citations for all references: 346 TCR
SCOPUS® Citations for all references: 483 TCR

Web of Science® Average Citations per reference: 27 ACR
SCOPUS® Average Citations per reference: 37 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 2017-09-21 12:42 in 31 seconds.




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


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