Abstract
The aim of this work is to show the capabilities of fuzzy modelling applied to a medical problem, the prediction of future cases of AIDS (Acquired Immune Deficiency Syndrome). An automatic knowledge acquisition is achieved from experimental data. This kind of modelling would be useful to practitioners and people not expert in modelling who need a set of fuzzy rules describing the behaviour of some system.
Two modelling techniques have been used in order to obtain the fuzzy models. The first approach is a neurofuzzy modelling technique based on ANFIS. And the second one is a fuzzy method that performs least squares identification and automatic rule generation by minimising an error index.
This work has been supported by the CICYT, Project TAP 99-0926-C04-02. Thanks to Hector de Arazoza for providing the mathematical model and Liuva M. Pedroso for generating the data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Arazoza, H., Loumes, R., Hoang, T., Interian, Y.: Modelling the HIV epidemicunder contact tracing. The Cuban case. Journal of Theoretical Medicine, 2 (2000) 267–274
Buckley, J. and Hayashi, Y.: Fuzzy input-output controllers are universal approximators, Fuzzy Sets and Systems 58 (1993) 273–278.
Garca-Cerezo, A.J., Lpez-Baldn, M.J, Mandow, A.: An Efficient Least Squares Fuzzy Modelling Method for Dynamic Systems, Proceed. CESA 96. IMACS Multiconf. Symp. Modelling, Analysis and Simulation. Lille. (1996) 885–890.
Hsieh, Y. H, de Arazoza, H, Lee S. M, Chen, CW: Estimating the number of Cubans infected sexually by human immunodeficiency virus using contact tracing data. International Journal of Epidemiology Vol 31, Iss 3 (2002) 679–683
Kosko, B.: Fuzzy systems as universal approximators, Proc. Int. Conf. on Fuzzy Systems, San Diego (1992)
Ljung, L.: System Identification Toolbox. For use with MATLAB. The Mathworks, Natick (Mass.) (1995)
Lopez-Baldan, M. J., Ruiz-Gomez J., Fernandez, R and Garcia-Cerezo, A., Input-Output Fuzzy Modelling Applied to a Mobile Robot. In Mastorakis, N. (ed.): Computational Intelligence and Applications. World Scientific and Engineering Society Press (1999) 283–288.
Roger Jang, J.-S., Sun, C.-T: Neuro-Fuzzy Modeling and Control. Proceed. IEEE, 83, No. 3, (1995) 378–406.
Ruiz-Gomez, J., Lopez-Baldan, M.J., Garcia-Cerezo, A.: Fuzzy Modelling of a Ternary Batch Distillation Column. Int. Journal of Computer Research 11, No 3 (2002) 347–355
Sugeno, M., Kang, G.T.: Fuzzy Modelling and Control of Multilayer Incinerator, Fuzzy Sets and Systems, 18 (1986) 329–346
Sugeno, M., Yasukawa, T.: A Fuzzy-Logic-Based Approach to Qualitative Modeling. IEEE Transactions on Fuzzy Systems 1, No 1 (1993) 7–31.
Takagi, T., Sugeno, M.,: Fuzzy Identification of Systems and Its Applicattion to Modeling and Control. IEEE Transactions on Systems, Man and Cybernetics, 15. (1985) 116–132.
Wang, Li-Xin: Fuzzy Systems are Universal Approximators. Proceed. Int. Conf. on Fuzzy Systems, San Diego (1992) 1163–1170.
Zardecki, A.: Rule-Based Forecasting. In: Pedricz, W. (ed.): Fuzzy Modelling. Paradigms and Practice. Kluwer. Boston Dordrecht London (1996) 375–391
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ruiz-Gomez, J., Lopcz-Baldan, M.J., Garcia-Ccrczo, A. (2003). Input-Output Fuzzy Identification of Nonlinear Multivariable Systems. Application to a Case of AIDS Spread Forecast. In: Mira, J., Álvarez, J.R. (eds) Artificial Neural Nets Problem Solving Methods. IWANN 2003. Lecture Notes in Computer Science, vol 2687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44869-1_61
Download citation
DOI: https://doi.org/10.1007/3-540-44869-1_61
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40211-4
Online ISBN: 978-3-540-44869-3
eBook Packages: Springer Book Archive