Parameter-Less Simulated Kalman Filter
Simulated Kalman Filter (SKF) algorithm is a new population-based metaheuristic optimization algorithm. In the original SKF algorithm, three parameter values are assigned during initialization, the initial error covariance, P(0), the process noise, Q, and the measurement noise, R. Further studies on...
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| Format: | Article |
| Language: | English |
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Penerbit UMP
2017
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| Online Access: | http://umpir.ump.edu.my/id/eprint/16999/ http://umpir.ump.edu.my/id/eprint/16999/1/61-286-1-PB.pdf |
| _version_ | 1848820320961560576 |
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| author | Nor Hidayati, Abdul Aziz Zuwairie, Ibrahim Nor Azlina, Ab. Aziz Saifudin, Razali |
| author_facet | Nor Hidayati, Abdul Aziz Zuwairie, Ibrahim Nor Azlina, Ab. Aziz Saifudin, Razali |
| author_sort | Nor Hidayati, Abdul Aziz |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Simulated Kalman Filter (SKF) algorithm is a new population-based metaheuristic optimization algorithm. In the original SKF algorithm, three parameter values are assigned during initialization, the initial error covariance, P(0), the process noise, Q, and the measurement noise, R. Further studies on the effect of P(0), Q and R values suggest that the SKF algorithm can be realized as a parameter-less algorithm. Instead of using constant values suggested for the parameters, this study uses random values for all three parameters, P(0), Q and R. Experimental results show that the parameter-less SKF managed to converge to near-optimal solution and performs as good as the original SKF algorithm. |
| first_indexed | 2025-11-15T02:07:35Z |
| format | Article |
| id | ump-16999 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:07:35Z |
| publishDate | 2017 |
| publisher | Penerbit UMP |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-169992018-02-08T02:46:15Z http://umpir.ump.edu.my/id/eprint/16999/ Parameter-Less Simulated Kalman Filter Nor Hidayati, Abdul Aziz Zuwairie, Ibrahim Nor Azlina, Ab. Aziz Saifudin, Razali QA Mathematics QA75 Electronic computers. Computer science Simulated Kalman Filter (SKF) algorithm is a new population-based metaheuristic optimization algorithm. In the original SKF algorithm, three parameter values are assigned during initialization, the initial error covariance, P(0), the process noise, Q, and the measurement noise, R. Further studies on the effect of P(0), Q and R values suggest that the SKF algorithm can be realized as a parameter-less algorithm. Instead of using constant values suggested for the parameters, this study uses random values for all three parameters, P(0), Q and R. Experimental results show that the parameter-less SKF managed to converge to near-optimal solution and performs as good as the original SKF algorithm. Penerbit UMP 2017-02 Article PeerReviewed application/pdf en cc_by http://umpir.ump.edu.my/id/eprint/16999/1/61-286-1-PB.pdf Nor Hidayati, Abdul Aziz and Zuwairie, Ibrahim and Nor Azlina, Ab. Aziz and Saifudin, Razali (2017) Parameter-Less Simulated Kalman Filter. International Journal of Software Engineering & Computer Sciences (IJSECS), 3. pp. 129-137. ISSN 2289-8522. (Published) http://ijsecs.ump.edu.my/index.php/archive/14-volume-3/24-ijsecs-3-009 doi: 10.15282/ijsecs.3.2017.9.0031 |
| spellingShingle | QA Mathematics QA75 Electronic computers. Computer science Nor Hidayati, Abdul Aziz Zuwairie, Ibrahim Nor Azlina, Ab. Aziz Saifudin, Razali Parameter-Less Simulated Kalman Filter |
| title | Parameter-Less Simulated Kalman Filter |
| title_full | Parameter-Less Simulated Kalman Filter |
| title_fullStr | Parameter-Less Simulated Kalman Filter |
| title_full_unstemmed | Parameter-Less Simulated Kalman Filter |
| title_short | Parameter-Less Simulated Kalman Filter |
| title_sort | parameter-less simulated kalman filter |
| topic | QA Mathematics QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/16999/ http://umpir.ump.edu.my/id/eprint/16999/ http://umpir.ump.edu.my/id/eprint/16999/ http://umpir.ump.edu.my/id/eprint/16999/1/61-286-1-PB.pdf |