http://www.cnr.it/ontology/cnr/individuo/prodotto/ID176560

Generating fuzzy models from deep knowledge: robustness and interpretability issues (Contributo in atti di convegno)

Type

Label

Generating fuzzy models from deep knowledge: robustness and interpretability issues (Contributo in atti di convegno) (literal)

Anno

Alternative label

Guglielmann R.; Ironi L. (2005)
Generating fuzzy models from deep knowledge: robustness and interpretability issues
in Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 8th European Conference ECSQARU 2005, Barcelona, July 6-8, 2005
(literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

Pagina inizio

Pagina fine

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana

Note

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

Generating fuzzy models from deep knowledge: robustness and interpretability issues (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume

Abstract

The most problematic and challenging issues in fuzzy modeling of nonlinear system dynamics deal with robustness and interpretability. Traditional data-driven approaches, especially when the data set is not adequate, may lead to a model that results to be either unable to reproduce the system dynamics or numerically unstable or unintelligible. This paper demonstrates that Qualitative Reasoning plays a crucial role to significantly improve both robustness and interpretability. In the modeling framework we propose both fuzzy partition of input output variables and the fuzzy rule base are built on the available deep knowledge represented through qualitative models. This leads to a clear and neat model structure that does describe the system dynamics, and the parameters of which have a physically significant meaning. Moreover, it allows us to properly constrain the parameter optimization problem, with a consequent gain in numerical stability. The obtained substantial improvement of model robustness and interpretability in \"actual\" physical terms lays the groundwork for new application perspectives of fuzzy models. (literal)

Editore