SYSTEM IDENTIFICATION OF NONLINEAR AUTOREGRESSIVE MODELS IN MONITORING DENGUE INFECTION

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International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

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Subject: Computational Science & Engineering, Engineering, Electrical & Electronic

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VOLUME 3 , ISSUE 4 (December 2010) > List of articles

SYSTEM IDENTIFICATION OF NONLINEAR AUTOREGRESSIVE MODELS IN MONITORING DENGUE INFECTION

H. Abdul Rahim * / F. Ibrahim / M. N. Taib

Keywords : dengue fever, NAR model, AIC, Lipschitz, FPE, ROC and AUC.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 3, Issue 4, Pages 783-806, DOI: https://doi.org/10.21307/ijssis-2017-421

License : (CC BY-NC-ND 4.0)

Published Online: 13-December-2017

ARTICLE

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.

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REFERENCES

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