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Input-Output Fuzzy Identification of Nonlinear Multivariable Systems. Application to a Case of AIDS Spread Forecast

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Artificial Neural Nets Problem Solving Methods (IWANN 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2687))

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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.

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© 2003 Springer-Verlag Berlin Heidelberg

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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

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  • DOI: https://doi.org/10.1007/3-540-44869-1_61

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40211-4

  • Online ISBN: 978-3-540-44869-3

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