System identification of nonlinear autoregressive models in monitoring dengue infection
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...
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Massey University
2010
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my.utm.384282017-02-15T01:49:29Z http://eprints.utm.my/id/eprint/38428/ System identification of nonlinear autoregressive models in monitoring dengue infection Abdul Rahim, Herlina Ibrahim, F. Taib, M. N. TK Electrical engineering. Electronics Nuclear engineering 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. Massey University 2010-12 Article PeerReviewed text/html en http://eprints.utm.my/id/eprint/38428/2/paper13.pdf Abdul Rahim, Herlina 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 1178-5608 |
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TK Electrical engineering. Electronics Nuclear engineering Abdul Rahim, Herlina Ibrahim, F. Taib, M. N. System identification of nonlinear autoregressive models in monitoring dengue infection |
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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. |
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Article |
author |
Abdul Rahim, Herlina Ibrahim, F. Taib, M. N. |
author_facet |
Abdul Rahim, Herlina Ibrahim, F. Taib, M. N. |
author_sort |
Abdul Rahim, Herlina |
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
|
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System identification of nonlinear autoregressive models in monitoring dengue infection
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title_sort |
system identification of nonlinear autoregressive models in monitoring dengue infection |
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Massey University |
publishDate |
2010 |
url |
http://eprints.utm.my/id/eprint/38428/2/paper13.pdf http://eprints.utm.my/id/eprint/38428/ |
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