<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

System Identification Of Nonlinear Autoregressive Models In Monitoring Dengue Infection

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This paper proposes system identification on application of nonlinear AR (NAR) based on Artificial Neural Network (ANN) for monitor of dengue infections. In...

System Identification Of Nonlinear Autoregressive Models In Monitoring Dengue Infection

Publié en ligne: 13 déc. 2017

Pages: 783 - 806

DOI: https://doi.org/10.21307/ijssis-2017-421

Mots clés
dengue fever, NAR model, AIC, Lipschitz, FPE, ROC and AUC

© 2010 H. Abdul Rahim et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

System Identification Of Nonlinear Autoregressive Models In Monitoring Dengue Infection

Publié en ligne: 13 déc. 2017

Pages: 783 - 806

DOI: https://doi.org/10.21307/ijssis-2017-421

Mots clésdengue fever, NAR model, AIC, Lipschitz, FPE, ROC and AUC

© 2010 H. Abdul Rahim et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
dengue fever, NAR model, AIC, Lipschitz, FPE, ROC and AUC