Affiliations: CISUC -- Centro de Informática e Sistemas da
Universidade de Coimbra, Department of Informatics Engineering, Pólo II of
the University of Coimbra, P 3030 Coimbra, Portugal
Abstract: A methodology and experimental comparison of neuro-fuzzy structures,
namely linguistic and zero and first-order Takagi-Sugeno, are developed. The
implementation of the model is conducted through the training of a neuro-fuzzy
network, i.e., a neural net architecture capable of representing a fuzzy
system. In the first phase, the structure of the model is obtained by
subtractive clustering, which allows the extraction of a set of relevant rules
based on a set of representative input-output data samples. Membership
functions with two-sided Gaussian functions are proposed and discussed. In the
second phase, the model parameters are tuned via the training of a neural
network. Furthermore, different fuzzy operators are compared, as well as
regular and two-sided Gaussian functions.
