  Fuzzy relational identification builds a relational model describing systems
behaviour by a nonlinear mapping between its variables. In this paper, we
propose a new fuzzy relational algorithm based on simplified max-min relational
equation. The algorithm presents an adaptation method applied to gravity-center
of each fuzzy set based on error integral value between measured and predicted
system output, and uses the concept of time-variant universe of discourses. The
identification algorithm also includes a method to attenuate noise influence in
extracted system relational model using a fuzzy filtering mechanism. The
algorithm is applied to one-step forward prediction of a simulated and
experimental motor drive system. The identified model has its input-output
variables (stator-reference current and motor speed signal) treated as fuzzy
sets, whereas the relations existing between them are described by means of a
matrix R defining the relational model extracted by the algorithm. The results
show the good potentialities of the algorithm in predict the behaviour of the
system and attenuate through the fuzzy filtering method possible noise
distortions in the relational model.
