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

Authors:
J. Ruiz-Gomez;M. J. Lopcz-Baldan;A. Garcia-Ccrczo
Affiliations:
System Engineering and Automation Department, University of Malaga E. T. S. Ingenieria Industrial, Malaga, Spain 29071;System Engineering and Automation Department, University of Malaga E. T. S. Ingenieria Industrial, Malaga, Spain 29071;System Engineering and Automation Department, University of Malaga E. T. S. Ingenieria Industrial, Malaga, Spain 29071
Venue:
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Year:
2009

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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.