Plamen Angelov, Edwin Lughofer,
"Data-Driven Evolving Fuzzy Systems using eTS and FLEXFIS: Comparative Analysis"
, in International Journal of General Systems, Vol. 37, Nummer 1, Taylor & Francis, Seite(n) 45-67, 2008
Data-Driven Evolving Fuzzy Systems using eTS and FLEXFIS: Comparative Analysis
Sprache des Titels:
In this paper two approaches for the incremental data-driven learning
of one of the most effective fuzzy model, namely of so-called Takagi-Sugeno type,
are compared. The algorithms that realize these approaches
include not only adaptation of linear parameters in fuzzy systems appearing in the rule consequents, but also incremental learning and evolution of premise parameters appearing in the membership functions (i.e. fuzzy
sets) in sample mode together with a rule learning strategy. In this sense the proposed
methods are 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 algorithms are included at the end of the
paper, where a comparison between conventional batch modelling methods for fuzzy systems
and the incremental learning methods demonstrated in this paper is made with respect to model qualities
and computation time. This evaluation is based on high dimensional data coming
from an industrial measuring process as well as from a
known source on the Internet, which underlines the usage of the new
method for fast online identification tasks.