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Qualitative Models and Fuzzy Systems: An Integrated Approach to System Identification

  • Riccardo Bellazzi
  • Raffaella Guglielmann
  • Liliana Ironi
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 18)

Abstract

We present a fuzzy-neuro method for the identification of nonlinear dynamical systems. The key idea which underlies our approach consists in the integration of qualitative modeling methods with fuzzy systems. The fuzzy model is initialized from rules which express the transition from one state to the next one. Such rules are automatically built by encoding the qualitative descriptions of the system dynamic behaviors inferred by the simulation of the qualitative model. The major advantage which derives from such an integrated framework lies in its capability both to represent the structural knowledge of the system at study and to determine, by exploiting the available experimental data, a functional approximation of the system dynamics that can be used as a reasonable predictor of the system’s future state. Results obtained by the application of our method for identification of the intracellular kinetics of Thiamine from data collected in the intestine cells will be discussed.

Keywords

Fuzzy System Fuzzy Rule Qualitative Model Fuzzy RUle Base Gaussian Membership Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Riccardo Bellazzi
    • 1
  • Raffaella Guglielmann
    • 2
  • Liliana Ironi
    • 3
  1. 1.Dipartimento di Informatica e SistemisticaUniversità di PaviaPaviaItaly
  2. 2.Dipartimento di MatematicaUniversità di PaviaPaviaItaly
  3. 3.Istituto di Analisi NumericaC.N.R.PaviaItaly

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