The Knowledge Engineering Review
Generating fuzzy models from deep knowledge: robustness and interpretability issues
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Learning from biomedical time series through the integration of qualitative models and fuzzy systems
Artificial Intelligence in Medicine
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The main problem in efficiently building robust fuzzy-neural models of nonlinear systems lies in the difficulty to define a “meaningful” fuzzy rule-base. Our approach to the solution of such a problem is based on a hybrid method which integrates fuzzy systems with qualitative models. We introduce qualitative models to exploit the available, although incomplete, a priori physical knowledge on the system with the goal to infer, through qualitative simulation, all of its possible behaviors. We show that a rule-base, which captures all of the distinctions in the system states, is automatically generated by encoding the knowledge of the system dynamics described by the outcomes of its qualitative simulation. Such a rule-base properly initializes a fuzzy identifier, which is then tuned to a set of experimental data. Our method has shown good performance when applied both as a predictor and as a simulator