A qualitative-fuzzy framework for nonlinear black-box system identification

Riccardo Bellazzi, Raffaella Guglielmann, Liliana Ironi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a novel approach to non-linear black-box system identification which combines Qualitative Reasoning (QR) methods with fuzzy logic systems. Such a method aims at building a good initialization of a fuzzy identifier, so that it will converge to the input-output relation which captures the nonlinear dynamics of the system. Fuzzy inference procedures should be initialized with a rule-base predefined by the human expert: when such a base is not available or poorly defined, the inference procedure becomes extremely inefficient. Our method aims at solving the problem of the construction of a meaningful rule-base: fuzzy rules are automatically generated by encoding the knowledge of the system dynamics described by the outcomes of its qualitative simulation. Both efficiency and robustness of the method are demonstrated by its application to the identification of the kinetics of Thiamine (vitamin B 1) and its phosphoesters in the cells of the intestine tissue.

Original languageEnglish
Title of host publicationIJCAI International Joint Conference on Artificial Intelligence
Pages1041-1046
Number of pages6
Volume2
Publication statusPublished - 1999
Event16th International Joint Conference on Artificial Intelligence, IJCAI 1999 - Stockholm, Sweden
Duration: Jul 31 1999Aug 6 1999

Other

Other16th International Joint Conference on Artificial Intelligence, IJCAI 1999
CountrySweden
CityStockholm
Period7/31/998/6/99

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Bellazzi, R., Guglielmann, R., & Ironi, L. (1999). A qualitative-fuzzy framework for nonlinear black-box system identification. In IJCAI International Joint Conference on Artificial Intelligence (Vol. 2, pp. 1041-1046)