System identification of nonlinear autoregressive models in monitoring dengue infection

Abdul Rahim, H. 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 11785608,

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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
Additional Information: Cited By (since 1996):2 Export Date: 29 January 2014 Source: Scopus Language of Original Document: English Correspondence Address: Abdul Rahim, H.; Department of Control and Instrumentation Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia; email: References: (1997) Dengue Haemorrhagic fever Diagnosis treatment, Prevention, and control, , W.H. Organization 2 nd ed. Geneva: WHO; Kyle, U.G., Bosaeus, I., De Lorenzo, A.D., Deurenberg, P., Elia, M., Gomez, J.M., Heitmann, B.L., Pichard, C., Biolectrical impedance analysis - part1:review of principles and methods (2004) Clinical Nutrition, 23, pp. 1226-1243; Skae, F.M., Dengue fever in Penang (1902) Br. Med. J, 2, pp. 1581-1582; Gordon, S.C., Dengue Fever Studies in Malaysia Institute for Medical Research Malaysia (1986) Bulletion, 23, pp. 1-5. , Dengue: an introduction. Rudnick A, Lim TW, Eds; (1998) Dengue Haemorrhagic Fever, , W. C. a. R. Annual Report, Dept. of Medical Microbiology, Fac. of Medicine, University of Malaya, 50603 Kuala Lumpur Malaysia; Ibrahim, F., Taib, M.N., Wan Abas, W.A.B., Guan, C.C., Sulaiman, S., A novel dengue fever (DF) and dengue haemorrhagic fever (DHF) analysis using artificial neural network (2005) Compu. Methods Programs Biomed., 79, pp. 273-281; Djuric, P.M., Kay, S.M., Order selection of autoregressive models (1992) IEEE Trans. On sig. Processing, 40 (11), pp. 2829-2833; Box, G.E.P., Jenkins, J.M., Time series analysis, forecating and control (1970) San Francisco:Holden Day; Akaike, H., A new look at the statistical model identification (1974) IEEE Trans. On Automat. Contr., 19, pp. 716-723; Rissanen, J., Modeling by shortest data descriotion (1978) Automatica, 14, pp. 465-478; Kashyap, R.L., Optimal choice of AR and MA parts in autoregressive moving average models (1982) IEEE Trans. Patt. Anal. Machine Intelligent, 14, pp. 99-104; Schwarz, G., Estimating the dimension of the model (1978) Ann. Stat., 6, pp. 461-464; Narenda, K.S., Parthasarathy, K., Identification and control of dynamical systems using neural networks (1990) IEEE Trans. On Nueral Networks, 1, pp. 4-27; He, X., Asada, H., A new method for identifying orders of input-output models for nonlinear dynamic systems (1993) Proc. of the American Control, pp. 2520-2523; Norgaard, M., Neural network based on system identification toolbox (2000) Technical Report, 00-E-891, , Department of Automation, Technical University of Denmark; Raganathan, A., The Levenberg-Marquardt Algorithm, , Georgia Institute of Technology, unpublished; Roweis, S., Levenberg-Marquardt Optimization, , Univ. of Toronto, unpublished; Adlassnig, K.P., Scheithauer, W., Performance evaluation of medical expert systems using ROC curves (1989) Compt. Biomed. Res., 22, pp. 297-313; Hanley, J.A., McNeil, B.J., The meaning and use of the area under a receiver operating characteristic (ROC) curve (1982) Radiology, 143, pp. 29-36; Downey, T.J., Meyer, D.J., Price, R.M., Spitznagel, E.L., (1999) Using the receiver operating characteristics to asses the performance of neural calssifiers, pp. 3642-3646; McNeil, B.J., Hanley, J.A., Statistical approaches to the analysis of receiver operating characteristic (ROC) curves (1984) Medical Decision Making, 4, p. 1984; Ibrahim, F., Prognosis of dengue fever and dengue haemorrhagic fever using bioelectrical impedance (2005) PhD Thesis, Department of Biomedical Engineering, , University of Malaya, July; (1997) Dengue Haemorrhagic fever Diagnosis treatment, Prevention, and control, , 2 nd ed. Geneva: World Health Organization; Chungue, E., Boutin, J.P., Roux, J., Antibody capture ELISA for IgM antibody titration in sera for dengue serodiagnosis and survellance (1989) Research in Virology, 140, pp. 229-240; Ibrahim, F., Ismail, N.A., Taib, M.N., Wan Abas, W.A.B., Modeling of hemoglobin in dengue fever and dengue hemorrhagic fever using biolectrical impedance (2004) Physiol. Meas., 25, pp. 607-615; Herlina, A.R., Fatimah, I., Mohd Nasir, T., A non-invasive system for predicting hemoglobin (Hb) in dengue fever (DF) and dengue hemorrhagic fever (DHF) (2005) Proc. Int. Conf. on Sensor and New Techniques in Pharmaceutical and Biomedical Research (ASIASENSE), , Kuala Lumpur; Abdul Rahim, H., Ibrahim, F., Taib, M.N., Modelling of hemoglobin in dengue infection application (2006) Journal of Electrical Engineering (ELEKTRIKA), 8, pp. 64-67; Albert, L., Impedance ratio in bioelectrical impedance measurements for body fluid shift determinitation (1998) Proc. Proc. of the IEEE 24th Annual Northeast Bioengineering, pp. 24-25
Uncontrolled Keywords: AIC, Dengue fever, FPE, Lipschitz, NAR model, ROC and AUC, Composite structures, Neural networks
Subjects: T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering
Depositing User: Mr Jenal S
Date Deposited: 02 Apr 2014 01:32
Last Modified: 01 Nov 2017 05:47

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