Simultaneous computation of model order and parameter estimation for system identification based on opposition-based simulated Kalman filter

System identification is a technique used to obtain a mathematical model of a system by performing analysis on input and output behavior of the system. Simultaneous Model Order and Parameter Estimation (SMOPE) has been proposed to address system identification problem efficiently using optimization...

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Main Authors:	Badaruddin, Muhammad, Kamil Zakwan, Mohd Azmi, Zuwairie, Ibrahim, Ahmad Afif, Mohd Faudzi, Pebrianti, Dwi
Format:	Conference or Workshop Item
Language:	English
Published:	IEEE 2018
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/21485/ http://umpir.ump.edu.my/id/eprint/21485/ http://umpir.ump.edu.my/id/eprint/21485/7/Simultaneous%20computation%20of%20model%20order-fkee-2018-1.pdf

id	ump-21485
recordtype	eprints
spelling	ump-214852018-07-13T03:22:57Z http://umpir.ump.edu.my/id/eprint/21485/ Simultaneous computation of model order and parameter estimation for system identification based on opposition-based simulated Kalman filter Badaruddin, Muhammad Kamil Zakwan, Mohd Azmi Zuwairie, Ibrahim Ahmad Afif, Mohd Faudzi Pebrianti, Dwi TK Electrical engineering. Electronics Nuclear engineering System identification is a technique used to obtain a mathematical model of a system by performing analysis on input and output behavior of the system. Simultaneous Model Order and Parameter Estimation (SMOPE) has been proposed to address system identification problem efficiently using optimization algorithms. The technique enables the computation of model order and parameters values to be done concurrently. The performance of SMOPE has been tested using particle swarm optimization (PSO) and gravitational search algorithm. However, the performance was worse than conventional ARX method. Current optimum opposition-based simulated Kalman filter (COOBSKF) is an improved version of simulated Kalman filter (SKF) which employs the concept of current optimum opposition-based learning (COOBL). Therefore, the objective of this paper is to test the effectiveness of the COOBSKF in solving system identification problem throughout SMOPE. Experiments are conducted on six system identification problems. The obtained outcomes showed that the performance of the SMOPE using COOBSKF is better than other SMOPE-based approaches. IEEE 2018 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/21485/7/Simultaneous%20computation%20of%20model%20order-fkee-2018-1.pdf Badaruddin, Muhammad and Kamil Zakwan, Mohd Azmi and Zuwairie, Ibrahim and Ahmad Afif, Mohd Faudzi and Pebrianti, Dwi (2018) Simultaneous computation of model order and parameter estimation for system identification based on opposition-based simulated Kalman filter. In: International Symposium On Control Systems (SICE ISCS), 9-11 March 2018 , Tokyo, Japan. pp. 105-112.. ISBN 978-4-907764-58-6 https://doi.org/10.23919/SICEISCS.2018.8330163
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Badaruddin, Muhammad Kamil Zakwan, Mohd Azmi Zuwairie, Ibrahim Ahmad Afif, Mohd Faudzi Pebrianti, Dwi Simultaneous computation of model order and parameter estimation for system identification based on opposition-based simulated Kalman filter
description	System identification is a technique used to obtain a mathematical model of a system by performing analysis on input and output behavior of the system. Simultaneous Model Order and Parameter Estimation (SMOPE) has been proposed to address system identification problem efficiently using optimization algorithms. The technique enables the computation of model order and parameters values to be done concurrently. The performance of SMOPE has been tested using particle swarm optimization (PSO) and gravitational search algorithm. However, the performance was worse than conventional ARX method. Current optimum opposition-based simulated Kalman filter (COOBSKF) is an improved version of simulated Kalman filter (SKF) which employs the concept of current optimum opposition-based learning (COOBL). Therefore, the objective of this paper is to test the effectiveness of the COOBSKF in solving system identification problem throughout SMOPE. Experiments are conducted on six system identification problems. The obtained outcomes showed that the performance of the SMOPE using COOBSKF is better than other SMOPE-based approaches.
format	Conference or Workshop Item
author	Badaruddin, Muhammad Kamil Zakwan, Mohd Azmi Zuwairie, Ibrahim Ahmad Afif, Mohd Faudzi Pebrianti, Dwi
author_facet	Badaruddin, Muhammad Kamil Zakwan, Mohd Azmi Zuwairie, Ibrahim Ahmad Afif, Mohd Faudzi Pebrianti, Dwi
author_sort	Badaruddin, Muhammad
title	Simultaneous computation of model order and parameter estimation for system identification based on opposition-based simulated Kalman filter
title_short	Simultaneous computation of model order and parameter estimation for system identification based on opposition-based simulated Kalman filter
title_full	Simultaneous computation of model order and parameter estimation for system identification based on opposition-based simulated Kalman filter
title_fullStr	Simultaneous computation of model order and parameter estimation for system identification based on opposition-based simulated Kalman filter
title_full_unstemmed	Simultaneous computation of model order and parameter estimation for system identification based on opposition-based simulated Kalman filter
title_sort	simultaneous computation of model order and parameter estimation for system identification based on opposition-based simulated kalman filter
publisher	IEEE
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/21485/ http://umpir.ump.edu.my/id/eprint/21485/ http://umpir.ump.edu.my/id/eprint/21485/7/Simultaneous%20computation%20of%20model%20order-fkee-2018-1.pdf
first_indexed	2023-09-18T22:31:33Z
last_indexed	2023-09-18T22:31:33Z
_version_	1777416319775277056

Simultaneous computation of model order and parameter estimation for system identification based on opposition-based simulated Kalman filter

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