A Genetic Algorithm Approach for Prediction of Linear Dynamical Systems
Modelling of linear dynamical systems is very important issue in science and engineering. The modelling process might be achieved by either the application of the governing laws describing the process or by using the input-output data sequence of the process. Most of the modelling algorithms reporte...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2013-01-01
|
Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2013/831657 |
id |
doaj-b5b434ff4feb4f078df7ae375af3f0ea |
---|---|
record_format |
Article |
spelling |
doaj-b5b434ff4feb4f078df7ae375af3f0ea2020-11-24T21:33:59ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472013-01-01201310.1155/2013/831657831657A Genetic Algorithm Approach for Prediction of Linear Dynamical SystemsZa'er Abo-Hammour0Othman Alsmadi1Shaher Momani2Omar Abu Arqub3Department of Mechatronics Engineering, Faculty of Engineering, The University of Jordan, Amman 11942, JordanDepartment of Electrical Engineering, Faculty of Engineering, The University of Jordan, Amman 11942, JordanDepartment of Mathematics, Faculty of Science, The University of Jordan, Amman 11942, JordanDepartment of Mathematics, Faculty of Science, Al Balqa Applied University, Salt 19117, JordanModelling of linear dynamical systems is very important issue in science and engineering. The modelling process might be achieved by either the application of the governing laws describing the process or by using the input-output data sequence of the process. Most of the modelling algorithms reported in the literature focus on either determining the order or estimating the model parameters. In this paper, the authors present a new method for modelling. Given the input-output data sequence of the model in the absence of any information about the order, the correct order of the model as well as the correct parameters is determined simultaneously using genetic algorithm. The algorithm used in this paper has several advantages; first, it does not use complex mathematical procedures in detecting the order and the parameters; second, it can be used for low as well as high order systems; third, it can be applied to any linear dynamical system including the autoregressive, moving-average, and autoregressive moving-average models; fourth, it determines the order and the parameters in a simultaneous manner with a very high accuracy. Results presented in this paper show the potentiality, the generality, and the superiority of our method as compared with other well-known methods.http://dx.doi.org/10.1155/2013/831657 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Za'er Abo-Hammour Othman Alsmadi Shaher Momani Omar Abu Arqub |
spellingShingle |
Za'er Abo-Hammour Othman Alsmadi Shaher Momani Omar Abu Arqub A Genetic Algorithm Approach for Prediction of Linear Dynamical Systems Mathematical Problems in Engineering |
author_facet |
Za'er Abo-Hammour Othman Alsmadi Shaher Momani Omar Abu Arqub |
author_sort |
Za'er Abo-Hammour |
title |
A Genetic Algorithm Approach for Prediction of Linear Dynamical Systems |
title_short |
A Genetic Algorithm Approach for Prediction of Linear Dynamical Systems |
title_full |
A Genetic Algorithm Approach for Prediction of Linear Dynamical Systems |
title_fullStr |
A Genetic Algorithm Approach for Prediction of Linear Dynamical Systems |
title_full_unstemmed |
A Genetic Algorithm Approach for Prediction of Linear Dynamical Systems |
title_sort |
genetic algorithm approach for prediction of linear dynamical systems |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2013-01-01 |
description |
Modelling of linear dynamical systems is very important issue in science and engineering. The modelling process might be achieved by either the application of the governing laws describing the process or by using the input-output data sequence of the process. Most of the modelling algorithms reported in the literature focus on either determining the order or estimating the model parameters. In this paper, the authors present a new method for modelling. Given the input-output data sequence of the model in the absence of any information about the order, the correct order of the model as well as the correct parameters is determined simultaneously using genetic algorithm. The algorithm used in this paper has several advantages; first, it does not use complex mathematical procedures in detecting the order and the parameters; second, it can be used for low as well as high order systems; third, it can be applied to any linear dynamical system including the autoregressive, moving-average, and autoregressive moving-average models; fourth, it determines the order and the parameters in a simultaneous manner with a very high accuracy. Results presented in this paper show the potentiality, the generality, and the superiority of our method as compared with other well-known methods. |
url |
http://dx.doi.org/10.1155/2013/831657 |
work_keys_str_mv |
AT zaerabohammour ageneticalgorithmapproachforpredictionoflineardynamicalsystems AT othmanalsmadi ageneticalgorithmapproachforpredictionoflineardynamicalsystems AT shahermomani ageneticalgorithmapproachforpredictionoflineardynamicalsystems AT omarabuarqub ageneticalgorithmapproachforpredictionoflineardynamicalsystems AT zaerabohammour geneticalgorithmapproachforpredictionoflineardynamicalsystems AT othmanalsmadi geneticalgorithmapproachforpredictionoflineardynamicalsystems AT shahermomani geneticalgorithmapproachforpredictionoflineardynamicalsystems AT omarabuarqub geneticalgorithmapproachforpredictionoflineardynamicalsystems |
_version_ |
1725950945911635968 |