Generating Fuzzy Models from Deep Knowledge: Robustness and Interpretability Issues
by
Raffaella Guglielmann and Liliana Ironi
Lecture Notes in Artificial Intelligence, 3571 , 600-612, 2005.
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.
%of fuzzy models.
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.
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Liliana Ironi
2005