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Experimental dataset to develop a parametric model based of DC geared motor in feeder machine

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 9 No. 3 (2019)

DOI
10.11591/ijece.v9i3.pp1576-1584
ISSN
2088-8708, 2088-8708
Terbit
2019-06-01
Penerbit
Institute of Advanced Engineering and Science
Lisensi
creativecommons.org/licenses/by-nc/4.0

Abstrak

This paper presents the application of a System Identification based on Particle Swarm Optimization (PSO) technique to develop parametric model of experimental dataset of DC Geared motor in feeder machine. The experimental was conducted to measure the input (voltage) and output (speed) data. The actual data is used to be optimized using PSO algorithm. The parameter emphasized is Time, Man Square Error (MSE) and Average Time. One of the best model has been chosen based on the optimum parameters.

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  • Tun Hussein Onn University of Malaysia
  • Mercu Buana University
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Rekan penulisOrganisasiArtikel bersama
Muhamad FitriMercu Buana University1
Imam HidayatMercu Buana University1
Nurato NuratoMercu Buana University1

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Sumber metadata: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, pertama di-harvest 27 Okt 2026, terakhir terlihat 8 Nov 2026.
Metadata sebagai oai_dc, dikonversi dari Crossref 
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Experimental dataset to develop a parametric model based of DC geared motor in feeder machine</dc:title><dc:creator>M., Azlan. W.</dc:creator><dc:creator>M., Salleh S.</dc:creator><dc:creator>S, Mahzan</dc:creator><dc:creator>A, Sadikin</dc:creator><dc:creator>S, Ahmad</dc:creator><dc:description>&lt;jats:p&gt;This paper presents the application of a System Identification based on Particle Swarm Optimization (PSO) technique to develop parametric model of experimental dataset of DC Geared motor in feeder machine. The experimental was conducted to measure the input (voltage) and output (speed) data. The actual data is used to be optimized using PSO algorithm. The parameter emphasized is Time, Man Square Error (MSE) and Average Time. One of the best model has been chosen based on the optimum parameters.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2019-06-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v9i3.pp1576-1584</dc:identifier><dc:identifier>10.11591/ijece.v9i3.pp1576-1584</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 9 No 3 (2019); 1576</dc:source><dc:source>2088-8708</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/17270/11982</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>