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 nu...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
2010
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| Subjects: | |
| 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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| Summary: | 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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