Separated Antecedent and Consequent Learning of Takagi-Sugeno Fuzzy Models
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of FUZZ-IEEE 2006
Original Kurzfassung:
In this paper a new algorithm for the learning of Takagi-Sugeno
fuzzy systems is introduced. In the algorithm different learning
techniques are applied for the antecedent and the consequent
parameters of the fuzzy system. We propose a hybrid method for the
antecedent parameters learning based on the combination of the
{\em Bacterial Evolutionary Algorithm (BEA)} and the {\em
Levenberg-Marquardt (LM)} method. For the linear parameters in
fuzzy systems appearing in the rule consequents the {\em Least
Squares (LS)} and the {\em Recursive Least Squares (RLS)}
techniques are applied, which will lead to a global optimal
solution of linear parameter vectors in the least squares sense.
Therefore a better performance can be guaranteed than with a
complete learning by BEA and LM. The paper is concluded by
evaluation results based on high-dimensional test data. These
evaluation results compare the new method with some conventional
fuzzy training methods with respect to approximation accuracy and
model complexity.
Sprache der Kurzfassung:
Deutsch
Erscheinungsjahr:
2006
Anzahl der Seiten:
8
Notiz zur Publikation:
Authors: Janos Botzheim, Edwin Lughofer, Erich Peter Klement, Laszlo Koczi and T.D. Gedeon
Forschungs-