http://www.cnr.it/ontology/cnr/individuo/prodotto/ID85170
Qualitative fuzzy system identification of complex dynamical systems (Contributo in atti di convegno)
- Type
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- Qualitative fuzzy system identification of complex dynamical systems (Contributo in atti di convegno) (literal)
- Anno
- 2007-01-01T00:00:00+01:00 (literal)
- Alternative label
Guglielmann R.; Ironi L. (2007)
Qualitative fuzzy system identification of complex dynamical systems
in 2007 IEEE Conference on Fuzzy Systems, Londra, U.K., 23-26 July, 2007
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Guglielmann R.; Ironi L. (literal)
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- Pagina fine
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
- 2007 IEEE International Conference on Fuzzy Systems (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
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- ISI Web of Science (WOS) (literal)
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- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- Dipartimento di Matematica, Universita` degli Studi di Pavia;
Istituto di Matematica Applicata e Tecnologie Informatiche (literal)
- Titolo
- Qualitative fuzzy system identification of complex dynamical systems (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autoriVolume
- Raffaella Guglielmann; Liliana Ironi (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
- Q. Shen;H. Hagras;R. John;T. Martin (literal)
- Abstract
- Fuzzy systems have been proved to be excellent candidates for system dynamics identification. However, they are affected by two drawbacks: the resulting nonlinear model (i) does not guarantee that the generalization property holds unless a large amount of samples is employed, and (ii) is not understandable from a physical viewpoint. These drawbacks are particularly serious when fuzzy identification deals with complex natural systems as the observational data set and/or empirical knowledge can occur to be inadequate. For these systems, the available knowledge of the underlying mechanisms is qualitative and highly incomplete, and does often prevent from formulating a quantitative differential model but not a qualitative one. This paper demonstrates that Qualitative Reasoning methods properly integrated with fuzzy systems yield a hybrid system identification method that overcomes the problems outlined above. (literal)
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