Opposition- based simulated kalman filters and their application in system identification

Metaheuristic optimization algorithms are well-established techniques to address those problems which are difficult to solve through traditional optimization methods. Among the various kinds of optimization algorithms, Simulated Kalman Filter (SKF) is a new population-based optimization algorithm in...

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Main Author:	Kamil Zakwan, Mohd Azmi
Format:	Thesis
Language:	English
Published:	2017
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/18150/19/Opposition-%20based%20simulated%20kalman%20filters%20and%20their%20application%20in%20system%20identification.pdf
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spelling	my-ump-ir.181502021-12-15T00:07:22Z Opposition- based simulated kalman filters and their application in system identification 2017-01 Kamil Zakwan, Mohd Azmi TK Electrical engineering. Electronics Nuclear engineering Metaheuristic optimization algorithms are well-established techniques to address those problems which are difficult to solve through traditional optimization methods. Among the various kinds of optimization algorithms, Simulated Kalman Filter (SKF) is a new population-based optimization algorithm inspired by the estimation capability of Kalman Filter. Based on the mechanism of Kalman filtering, the SKF includes prediction, measurement, and estimation process to search for global optimum. The SKF has been shown to yield good performance in solving benchmark optimization problems. However, the exploration capability of SKF could be further improved. From literature, Opposition-based Learning (OBL) has been employed to increase the diversity (exploration) of search algorithm by allowing current population to be compared with an opposite population. By employing this concept, more potential agents are generated to explore more promising regions that exist in the solution domain. Therefore, this research intends to improve the exploration capability of SKF through the application of OBL. The OBL is employed after the estimation process of SKF. Two versions of OBL techniques have been considered in this research, which are original OBL and Current Optimum Opposition-based Learning (COOBL). Experimental results over the IEEE Congress on Evolutionary Computation (CEC) 2014 benchmark functions indicate that Opposition-based Simulated Kalman Filter (OBSKF) has made some improvement towards exploration capability of SKF, while the Current Optimum Opposition-based Simulated Kalman Filter (COOBSKF) improved the exploration capability of SKF significantly. The COOBSKF also has been compared with five other optimization algorithms and outperforms them all. Besides that, this thesis also presents the application of COOBSKF in a system identification problem. The overall performance is evaluated based on six case studies. According to the experimental results, COOBSKF provides average of maximum model validation up to 90%. This technique can be an alternative approach to solve system identification problem, apart from using conventional method. 2017-01 Thesis http://umpir.ump.edu.my/id/eprint/18150/ http://umpir.ump.edu.my/id/eprint/18150/19/Opposition-%20based%20simulated%20kalman%20filters%20and%20their%20application%20in%20system%20identification.pdf pdf en public masters Universiti Malaysia Pahang Faculty of Electrical and Electronics Engineering
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
collection	UMPSA Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Kamil Zakwan, Mohd Azmi Opposition- based simulated kalman filters and their application in system identification
description	Metaheuristic optimization algorithms are well-established techniques to address those problems which are difficult to solve through traditional optimization methods. Among the various kinds of optimization algorithms, Simulated Kalman Filter (SKF) is a new population-based optimization algorithm inspired by the estimation capability of Kalman Filter. Based on the mechanism of Kalman filtering, the SKF includes prediction, measurement, and estimation process to search for global optimum. The SKF has been shown to yield good performance in solving benchmark optimization problems. However, the exploration capability of SKF could be further improved. From literature, Opposition-based Learning (OBL) has been employed to increase the diversity (exploration) of search algorithm by allowing current population to be compared with an opposite population. By employing this concept, more potential agents are generated to explore more promising regions that exist in the solution domain. Therefore, this research intends to improve the exploration capability of SKF through the application of OBL. The OBL is employed after the estimation process of SKF. Two versions of OBL techniques have been considered in this research, which are original OBL and Current Optimum Opposition-based Learning (COOBL). Experimental results over the IEEE Congress on Evolutionary Computation (CEC) 2014 benchmark functions indicate that Opposition-based Simulated Kalman Filter (OBSKF) has made some improvement towards exploration capability of SKF, while the Current Optimum Opposition-based Simulated Kalman Filter (COOBSKF) improved the exploration capability of SKF significantly. The COOBSKF also has been compared with five other optimization algorithms and outperforms them all. Besides that, this thesis also presents the application of COOBSKF in a system identification problem. The overall performance is evaluated based on six case studies. According to the experimental results, COOBSKF provides average of maximum model validation up to 90%. This technique can be an alternative approach to solve system identification problem, apart from using conventional method.
format	Thesis
qualification_level	Master's degree
author	Kamil Zakwan, Mohd Azmi
author_facet	Kamil Zakwan, Mohd Azmi
author_sort	Kamil Zakwan, Mohd Azmi
title	Opposition- based simulated kalman filters and their application in system identification
title_short	Opposition- based simulated kalman filters and their application in system identification
title_full	Opposition- based simulated kalman filters and their application in system identification
title_fullStr	Opposition- based simulated kalman filters and their application in system identification
title_full_unstemmed	Opposition- based simulated kalman filters and their application in system identification
title_sort	opposition- based simulated kalman filters and their application in system identification
granting_institution	Universiti Malaysia Pahang
granting_department	Faculty of Electrical and Electronics Engineering
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/18150/19/Opposition-%20based%20simulated%20kalman%20filters%20and%20their%20application%20in%20system%20identification.pdf
_version_	1783732027788361728

Opposition- based simulated kalman filters and their application in system identification

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