Universiti Teknologi Malaysia Institutional Repository

System identification of nonlinear autoregressive models in monitoring dengue infection

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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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.

Item Type:Article
Uncontrolled Keywords:dengue fever, NAR model, AIC, Lipschitz, FPE, ROC and AUC
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:38428
Deposited On:26 May 2014 08:46
Last Modified:15 Feb 2017 09:49

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