  A fuzzy controller is usually designed by formulating the knowledge of a
human expert into a set of linguistic variables and fuzzy rules. Among the most
successful methods to automate the fuzzy controllers development process are
evolutionary algorithms. In this work, we propose the Recurrent Fuzzy Voronoi
(RFV) model, a representation for recurrent fuzzy systems. It is an extension
of the FV model proposed by Kavka and Schoenauer that extends the application
domain to include temporal problems. The FV model is a representation for fuzzy
controllers based on Voronoi diagrams that can represent fuzzy systems with
synergistic rules, fulfilling the $\epsilon$-completeness property and
providing a simple way to introduce a priory knowledge. In the proposed
representation, the temporal relations are embedded by including internal units
that provide feedback by connecting outputs to inputs. These internal units act
as memory elements. In the RFV model, the semantic of the internal units can be
specified together with the a priori rules. The geometric interpretation of the
rules allows the use of geometric variational operators during the evolution.
The representation and the algorithms are validated in two problems in the area
of system identification and evolutionary robotics.
