Bibtex entry:

@article{ieeeTFS:Rov96,
  key  ={ieeeTFS:Rov96},
  title={Fuzzy Sets of Rules for System Identification},
  author={R. Rovatti and R. Guerrieri},
  journal={IEEE Trans. on Fuzzy Systems},
  volume={4},
  number={2},
  month={May},
  pages={89-102},
  year={1996},
  keywords={fuzzy algorithm},
  abstract={The synthesis of fuzzy systems involves the identification of a
structure and its specialization by means of parameter optimization.
In doing this, symbolic approaches which encode the structure
information in the form of high-level rules allow further manipulation
of the system to minimize its complexity and possibly its
implementation cost while all-parametric methodologies often achieve
better approximation performance.  In this paper we rely on the
concept of fuzzy set of rules to tackle the rule induction problem at
an intermediate level.  An on-line adaptive algorithm is developed
which almost surely learns the extent to which inclusion of a rule in
the rule set significantly contributes to the reproduction of the
target behavior.  Then, the resulting fuzzy set of rules can be
defuzzified to give a conventional rule set with similar behavior.
Comparisons with high-level and low-level methodologies show that this
approach retains most positive features of both.
},
}

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