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.
Download Postscript file.
Back to publication list
Liliana Ironi
1999