Qualitative models and fuzzy systems: an integrated approach for learning from data.
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.
Dipartimento di Informatica e Sistemistica, Università di Pavia, Italy. ric@ipvaimed3.unipv.it
R
R
Bellazzi
L
L
Ironi
R
R
Guglielmann
M
M
Stefanelli
D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D003198:Computer Simulation; D003922:Diabetes Mellitus, Type 1 / Q000188:drug therapy; D005103:Expert Systems; D005544:Forecasting; D017143:Fuzzy Logic; D006801:Humans; D007328:Insulin / Q000627:therapeutic use; D008954:Models, Biological; D017711:Nonlinear Dynamics; D011379:Prognosis; D015203:Reproducibility of Results
D007328:Insulin