Qualitative models and fuzzy systems: an integrated approach for learning from data
by R. Bellazzi, L. Ironi, R. Guglielmann, M. Stefanelli
Artificial Intelligence in Medicine
ABSTRACT
This paper presents a method for the identification of the dynamics of
non-linear systems by learning from data. The key idea which underlies
our approach consists of the integration of qualitative modeling
techniques with fuzzy logic systems. The resulting hybrid method
exploits the a priori structural knowledge on the system to initialize
a fuzzy inference procedure which determines, from the available
experimental data, a functional approximation of the system dynamics
that can be used as a reasonable predictor of the patient's future
state. The major advantage which results from such an integrated
framework lies in a significant improvement of both efficiency and
robustness of identification methods based on fuzzy models which learn
an input-output relation from data. As a benchmark of our method, we
have considered the problem of identifying the response to the insulin
therapy from insulin-dependent diabetic patients: the results obtained
are presented and discussed in the paper.
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