How to improve fuzzy-neural system modeling by means of qualitative simulation

by R. Bellazzi, R. Guglielmann, L. Ironi



IEEE Trans. on Neural Networks, to appear


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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    Liliana Ironi 1999