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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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  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 in 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: 1,035 | Views: 4,422

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

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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 327] [SCOPUS Times Cited 440]

[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:

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

[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 72] [SCOPUS Times Cited 94]

[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 68] [SCOPUS Times Cited 80]

[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: 467 TCR
SCOPUS® Citations for all references: 614 TCR

Web of Science® Average Citations per reference: 36 ACR
SCOPUS® Average Citations per reference: 47 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 2021-03-03 09:58 in 31 seconds.

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