Staff View: System identification of nonlinear autoregressive models in monitoring dengue infection

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Main Authors:	Abdul Rahim, H., Ibrahim, F., Taib, M.N.
Format:	Article
Language:	English
Published:	2010
Subjects:	T Technology (General) TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.um.edu.my/9348/1/System_identification_of_nonlinear_autoregressive_models_in_monitoring_dengue_infection.pdf http://eprints.um.edu.my/9348/ http://www.scopus.com/inward/record.url?eid=2-s2.0-79551508977&partnerID=40&md5=0491420521e068f90b645b6f6da2cc77 www-ist.massey.ac.nz/s2is/Issues/v3/n4/papers/paper13.pdf www.s2is.org/Issues/v3/n4/papers/paper13.pdf
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spelling	my.um.eprints.93482017-11-01T05:47:35Z http://eprints.um.edu.my/9348/ System identification of nonlinear autoregressive models in monitoring dengue infection Abdul Rahim, H. Ibrahim, F. Taib, M.N. T Technology (General) TA Engineering (General). Civil engineering (General) This paper proposes system identification on application of nonlinear AR (NAR) based on Artificial Neural Network (ANN) for monitor of dengue infections. In building the model, three selection criteria, i.e. the final prediction error (FPE), Akaike's Information Criteria (AIC), and Lipschitz number were used. Each of the models is divided into two approaches, which are unregularized approach and regularized approach. The findings indicate that NARMAX model with regularized approach yields better accuracy by 80.60. The best parameters' settings for this thesis can be found using the Lipschitz number criterion for the model order selection with artificial neural network structure of 4 trained using the Levenberg Marquardt algorithm. 2010 Article PeerReviewed application/pdf en http://eprints.um.edu.my/9348/1/System_identification_of_nonlinear_autoregressive_models_in_monitoring_dengue_infection.pdf Abdul Rahim, H. and Ibrahim, F. and Taib, M.N. (2010) System identification of nonlinear autoregressive models in monitoring dengue infection. International Journal on Smart Sensing and Intelligent Systems, 3 (4). pp. 783-806. ISSN 11785608 http://www.scopus.com/inward/record.url?eid=2-s2.0-79551508977&partnerID=40&md5=0491420521e068f90b645b6f6da2cc77 www-ist.massey.ac.nz/s2is/Issues/v3/n4/papers/paper13.pdf www.s2is.org/Issues/v3/n4/papers/paper13.pdf
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
language	English
topic	T Technology (General) TA Engineering (General). Civil engineering (General)
spellingShingle	T Technology (General) TA Engineering (General). Civil engineering (General) Abdul Rahim, H. Ibrahim, F. Taib, M.N. System identification of nonlinear autoregressive models in monitoring dengue infection
description	This paper proposes system identification on application of nonlinear AR (NAR) based on Artificial Neural Network (ANN) for monitor of dengue infections. In building the model, three selection criteria, i.e. the final prediction error (FPE), Akaike's Information Criteria (AIC), and Lipschitz number were used. Each of the models is divided into two approaches, which are unregularized approach and regularized approach. The findings indicate that NARMAX model with regularized approach yields better accuracy by 80.60. The best parameters' settings for this thesis can be found using the Lipschitz number criterion for the model order selection with artificial neural network structure of 4 trained using the Levenberg Marquardt algorithm.
format	Article
author	Abdul Rahim, H. Ibrahim, F. Taib, M.N.
author_facet	Abdul Rahim, H. Ibrahim, F. Taib, M.N.
author_sort	Abdul Rahim, H.
title	System identification of nonlinear autoregressive models in monitoring dengue infection
title_short	System identification of nonlinear autoregressive models in monitoring dengue infection
title_full	System identification of nonlinear autoregressive models in monitoring dengue infection
title_fullStr	System identification of nonlinear autoregressive models in monitoring dengue infection
title_full_unstemmed	System identification of nonlinear autoregressive models in monitoring dengue infection
title_sort	system identification of nonlinear autoregressive models in monitoring dengue infection
publishDate	2010
url	http://eprints.um.edu.my/9348/1/System_identification_of_nonlinear_autoregressive_models_in_monitoring_dengue_infection.pdf http://eprints.um.edu.my/9348/ http://www.scopus.com/inward/record.url?eid=2-s2.0-79551508977&partnerID=40&md5=0491420521e068f90b645b6f6da2cc77 www-ist.massey.ac.nz/s2is/Issues/v3/n4/papers/paper13.pdf www.s2is.org/Issues/v3/n4/papers/paper13.pdf
_version_	1643688538790690816
score	13.211869

System identification of nonlinear autoregressive models in monitoring dengue infection

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