Parameter-Less Simulated Kalman Filter

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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Main Authors: Nor Hidayati, Abdul Aziz, Zuwairie, Ibrahim, Nor Azlina, Ab. Aziz, Saifudin, Razali
Format: Article
Language:English
Published: Penerbit UMP 2017
Subjects:
QA Mathematics
QA75 Electronic computers. Computer science
Online Access: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
id ump-16999
recordtype eprints
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 http://ijsecs.ump.edu.my/index.php/archive/14-volume-3/24-ijsecs-3-009 doi: 10.15282/ijsecs.3.2017.9.0031
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic QA Mathematics
QA75 Electronic computers. Computer science
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
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.
format Article
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
title Parameter-Less Simulated Kalman Filter
title_short 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_sort parameter-less simulated kalman filter
publisher Penerbit UMP
publishDate 2017
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
first_indexed 2023-09-18T22:23:09Z
last_indexed 2023-09-18T22:23:09Z
_version_ 1777415791157706752

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