Genetic algorithm for system modelling

Olteanu Marius; Paraschiv Nicolae; Koprinkova Petia; Todorov Yancho

doi:10.1109/ECAI.2017.8166517

Genetic algorithm for system modelling

Olteanu Marius, Paraschiv Nicolae, Koprinkova Petia, Todorov Yancho

Not published at VTT

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

1 Citation (Scopus)

Abstract

The process of modeling of a real system usually implies an iterative approach where an initial model is incrementally modified in order to increase its accuracy with regard to available experimental data. Many approaches are discussed in the literature, among which some are based on Artificial Intelligence techniques. Continuous improvement in the mathematical model adequacy is very similar with the principal of genetic evolution applied in computational intelligence methods. The present paper investigates the application of genetic algorithm technique to optimize the model parameters for an industrial process.

Original language	English
Title of host publication	Proceedings of the 9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	1-4
Number of pages	4
ISBN (Electronic)	978-1-50906457-1
DOIs	https://doi.org/10.1109/ECAI.2017.8166517
Publication status	Published - 4 Dec 2017
MoE publication type	A4 Article in a conference publication
Event	9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017 - Targoviste, Romania Duration: 29 Jun 2017 → 1 Jul 2017

Conference

Conference	9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017
Country/Territory	Romania
City	Targoviste
Period	29/06/17 → 1/07/17

Keywords

Chemical process
Genetic algorithm
System identification

Access to Document

10.1109/ECAI.2017.8166517

Cite this

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title = "Genetic algorithm for system modelling",

abstract = "The process of modeling of a real system usually implies an iterative approach where an initial model is incrementally modified in order to increase its accuracy with regard to available experimental data. Many approaches are discussed in the literature, among which some are based on Artificial Intelligence techniques. Continuous improvement in the mathematical model adequacy is very similar with the principal of genetic evolution applied in computational intelligence methods. The present paper investigates the application of genetic algorithm technique to optimize the model parameters for an industrial process.",

keywords = "Chemical process, Genetic algorithm, System identification",

author = "Olteanu Marius and Paraschiv Nicolae and Koprinkova Petia and Todorov Yancho",

year = "2017",

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doi = "10.1109/ECAI.2017.8166517",

language = "English",

pages = "1--4",

booktitle = "Proceedings of the 9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017",

publisher = "IEEE Institute of Electrical and Electronic Engineers",

address = "United States",

note = "9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017 ; Conference date: 29-06-2017 Through 01-07-2017",

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Marius, O, Nicolae, P, Petia, K & Yancho, T 2017, Genetic algorithm for system modelling. in Proceedings of the 9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017. IEEE Institute of Electrical and Electronic Engineers, pp. 1-4, 9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017, Targoviste, Romania, 29/06/17. https://doi.org/10.1109/ECAI.2017.8166517

Genetic algorithm for system modelling. / Marius, Olteanu; Nicolae, Paraschiv; Petia, Koprinkova et al.
Proceedings of the 9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017. IEEE Institute of Electrical and Electronic Engineers, 2017. p. 1-4.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

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AU - Nicolae, Paraschiv

AU - Petia, Koprinkova

AU - Yancho, Todorov

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Y1 - 2017/12/4

N2 - The process of modeling of a real system usually implies an iterative approach where an initial model is incrementally modified in order to increase its accuracy with regard to available experimental data. Many approaches are discussed in the literature, among which some are based on Artificial Intelligence techniques. Continuous improvement in the mathematical model adequacy is very similar with the principal of genetic evolution applied in computational intelligence methods. The present paper investigates the application of genetic algorithm technique to optimize the model parameters for an industrial process.

AB - The process of modeling of a real system usually implies an iterative approach where an initial model is incrementally modified in order to increase its accuracy with regard to available experimental data. Many approaches are discussed in the literature, among which some are based on Artificial Intelligence techniques. Continuous improvement in the mathematical model adequacy is very similar with the principal of genetic evolution applied in computational intelligence methods. The present paper investigates the application of genetic algorithm technique to optimize the model parameters for an industrial process.

KW - Chemical process

KW - Genetic algorithm

KW - System identification

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U2 - 10.1109/ECAI.2017.8166517

DO - 10.1109/ECAI.2017.8166517

M3 - Conference article in proceedings

AN - SCOPUS:85043330081

SP - 1

EP - 4

BT - Proceedings of the 9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017

PB - IEEE Institute of Electrical and Electronic Engineers

T2 - 9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017

Y2 - 29 June 2017 through 1 July 2017

ER -

Genetic algorithm for system modelling

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