Erich Klement, Edwin Lughofer,
"FLEXFIS: A Variant for Incremental Learning of Takagi-Sugeno Fuzzy Systems"
: Proceedings FUZZ-IEEE 2005, 5-2005
Original Titel:
FLEXFIS: A Variant for Incremental Learning of Takagi-Sugeno Fuzzy Systems
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings FUZZ-IEEE 2005
Original Kurzfassung:
In this paper a new algorithm for the incremental learning of specific
data-driven models, namely so-called Takagi-Sugeno fuzzy systems,
is introduced. The new open-loop learning approach
includes not only adaptation of linear parameters in fuzzy systems
appearing in the rule consequents, but also sample mode adaptation
of premise parameters appearing in the membership functions (i.e. fuzzy
sets) together with a rule learning strategy. In this sense the proposed
method is applicable for fast model training tasks in various industrial processes,
whenever there is a demand of online system identification in order to apply models
representing nonlinear system behaviors to system monitoring,
online fault detection or open-loop control.
An evaluation of the incremental learning algorithm is included at the end of the
paper, where a comparison between conventional closed-loop modelling methods
for fuzzy systems and the incremental learning method (also called adaptation in open-loop)
demonstrated in this paper is made with respect to model qualities
and computation time. This evaluation will be based on high dimensional data coming
from an industrial measuring process as well as from a
known source in the internet, which should underline the usage of the new
method for fast online identification tasks.
Forschungs-