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...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2018
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/21485/7/Simultaneous%20computation%20of%20model%20order-fkee-2018-1.pdf |
| _version_ | 1796992773191106560 |
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| 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 |
| collection | UMP |
| 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. |
| first_indexed | 2024-03-06T12:24:39Z |
| format | Conference or Workshop Item |
| id | UMPir21485 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:24:39Z |
| publishDate | 2018 |
| publisher | IEEE |
| record_format | dspace |
| spelling | UMPir214852018-07-13T03:22:57Z 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 |
| 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 |
| title | 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_short | 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 |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/21485/7/Simultaneous%20computation%20of%20model%20order-fkee-2018-1.pdf |
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