Open AccessExperimental dataset to develop a parametric model based of DC geared motor in feeder machine
Author(s) -
W. M. Azlan.,
Salihatun Md Salleh,
Shahruddin Mahzan,
Azmahani Sadikin,
Sufizar Ahmad
Publication year - 2019
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v9i3.pp1576-1584
Subject(s) - particle swarm optimization , computer science , parametric statistics , dc motor , measure (data warehouse) , parametric model , voltage , mean squared error , system identification , identification (biology) , control theory (sociology) , algorithm , artificial intelligence , engineering , data mining , mathematics , statistics , control (management) , botany , biology , electrical engineering
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.