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Performance Of Parameter-Magnitude Based Information Criterion In Identification Of Linear Discrete-Time Model

Information criterion is an important factor for model structure selection in system identification. It is used to determine the optimality of a particular model structure with the aim of selecting an adequate model. There had not been, or scarcely have been, any loss function that evaluates parsimo...

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Main Authors: Abd Samad, Md Fahmi, Mohd Nasir, Abdul Rahman
Format: Article
Language:English
Published: J Fundam Appl Sci. (Faculty Of Sciences And Technology Of The University Of El Oued, Algeria) 2018
Subjects:
Q Science (General)
QA76 Computer software
Online Access:http://eprints.utem.edu.my/id/eprint/22792/2/3899-8265-1-PB_CMA_ICMSC_JFAS.pdf
http://eprints.utem.edu.my/id/eprint/22792/
http://jfas.info/psjfas/index.php/jfas/article/view/3899/2219
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spelling my.utem.eprints.227922021-09-06T17:23:15Z http://eprints.utem.edu.my/id/eprint/22792/ Performance Of Parameter-Magnitude Based Information Criterion In Identification Of Linear Discrete-Time Model Abd Samad, Md Fahmi Mohd Nasir, Abdul Rahman Q Science (General) QA76 Computer software Information criterion is an important factor for model structure selection in system identification. It is used to determine the optimality of a particular model structure with the aim of selecting an adequate model. There had not been, or scarcely have been, any loss function that evaluates parsimony of model structures (bias contribution) based on the magnitude of parameter or coefficient. The magnitude of parameter could have a big role in choosing whether a term is significant enough to be included in a model and justifies ones' judgement in choosing or discarding a term/variable. This study intends to develop a new information criterion such that the bias contribution is related not only to the number of parameters, but mainly to the magnitude of the parameters. The parameter-magnitude based information criterion (PMIC2) is demonstrated in identification of linear discrete time model. The demonstration is tested using computational software on a number of simulated systems in the form of discrete-time linear regressive models of various lag orders and number of term/variables. It is shown that PMIC2 is able to select the correct the model based on all of the tested datasets. J Fundam Appl Sci. (Faculty Of Sciences And Technology Of The University Of El Oued, Algeria) 2018 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/22792/2/3899-8265-1-PB_CMA_ICMSC_JFAS.pdf Abd Samad, Md Fahmi and Mohd Nasir, Abdul Rahman (2018) Performance Of Parameter-Magnitude Based Information Criterion In Identification Of Linear Discrete-Time Model. Journal Of Fundamental And Applied Sciences, 10 (3S). pp. 345-354. ISSN 1112-9867 http://jfas.info/psjfas/index.php/jfas/article/view/3899/2219
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
topic Q Science (General)
QA76 Computer software
spellingShingle Q Science (General)
QA76 Computer software
Abd Samad, Md Fahmi
Mohd Nasir, Abdul Rahman
Performance Of Parameter-Magnitude Based Information Criterion In Identification Of Linear Discrete-Time Model
description Information criterion is an important factor for model structure selection in system identification. It is used to determine the optimality of a particular model structure with the aim of selecting an adequate model. There had not been, or scarcely have been, any loss function that evaluates parsimony of model structures (bias contribution) based on the magnitude of parameter or coefficient. The magnitude of parameter could have a big role in choosing whether a term is significant enough to be included in a model and justifies ones' judgement in choosing or discarding a term/variable. This study intends to develop a new information criterion such that the bias contribution is related not only to the number of parameters, but mainly to the magnitude of the parameters. The parameter-magnitude based information criterion (PMIC2) is demonstrated in identification of linear discrete time model. The demonstration is tested using computational software on a number of simulated systems in the form of discrete-time linear regressive models of various lag orders and number of term/variables. It is shown that PMIC2 is able to select the correct the model based on all of the tested datasets.
format Article
author Abd Samad, Md Fahmi
Mohd Nasir, Abdul Rahman
author_facet Abd Samad, Md Fahmi
Mohd Nasir, Abdul Rahman
author_sort Abd Samad, Md Fahmi
title Performance Of Parameter-Magnitude Based Information Criterion In Identification Of Linear Discrete-Time Model
title_short Performance Of Parameter-Magnitude Based Information Criterion In Identification Of Linear Discrete-Time Model
title_full Performance Of Parameter-Magnitude Based Information Criterion In Identification Of Linear Discrete-Time Model
title_fullStr Performance Of Parameter-Magnitude Based Information Criterion In Identification Of Linear Discrete-Time Model
title_full_unstemmed Performance Of Parameter-Magnitude Based Information Criterion In Identification Of Linear Discrete-Time Model
title_sort performance of parameter-magnitude based information criterion in identification of linear discrete-time model
publisher J Fundam Appl Sci. (Faculty Of Sciences And Technology Of The University Of El Oued, Algeria)
publishDate 2018
url http://eprints.utem.edu.my/id/eprint/22792/2/3899-8265-1-PB_CMA_ICMSC_JFAS.pdf
http://eprints.utem.edu.my/id/eprint/22792/
http://jfas.info/psjfas/index.php/jfas/article/view/3899/2219
_version_ 1710679436128419840
score 13.211869

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