PROCEEDINGS IPMU '08
Linguistic Approximation of TSK Fuzzy Models with Multi-objective Neuro-Evolutionary Algorithms
G. Sánchez, J.F. Sánchez, J.M. Alcaraz, F. Jiménez.
In this paper, a multi-objective constrained
optimization model is proposed
to improve interpretability of
TSK fuzzy models. This approach
allows a linguistic approximation of
the fuzzy models. Three different
multi-objective evolutionary algorithms
(MONEA, ENORA and
NSGA-II) are used together with
neural network techniques. These
algorithms are checked out in the approximation
of a dynamic non-linear
system studied in literature. The
results clearly show a real ability
and effectiveness of the proposed approach
to find accurate and interpretable
TSK fuzzy models.
 | PDF full paper |