How to improve fuzzy-neural system modeling by means of qualitative simulation
by R. Bellazzi, R. Guglielmann, L. Ironi
IEEE Transactions on Neural Networks
ABSTRACT
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 all the available a priori physical knowledge on the
system with the goal to infer, through qualitative simulation, all of its possible behaviors. We show here 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.
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