Interpretability Improvement of Data-Driven Evolving Fuzzy Systems
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings EUSFLAT 2005
Original Kurzfassung:
In this paper complexity reduction strategies and algorithms are investigated
in connection with data-driven incremental learning approaches
for specific Takagi-Sugeno fuzzy systems, also called {\em Fuzzy Basis Function Networks}.
%The main focus will be attended to {\em FLEXFIS}, a specific variant for an incremental learning of this
%kind of fuzzy systems, but in principle
%also called {\em FLEXFIS} \cite{LughoferKlement05}.
Hereby, the task lies in the improvement of linguistic interpretability
of the evolving fuzzy systems not only within an offline post processing phase, i.e. after
the complete data-driven modelling process, but also in online mode, i.e. after
each incremental learning step. The latter ensures a transparency of the so far
trained fuzzy models, which can be an essential requirement for online monitoring
tasks, where transparent rules may act as useful explanation for actual system states.
The paper is concluded with an evaluation of the proposed methods on high dimensional data coming
from an industrial measuring processes, where purely evolving fuzzy systems are compared against
linguistically improved evolving fuzzy systems with respect to approximation quality and model complexity.
Sprache der Kurzfassung:
Deutsch
Erscheinungsmonat:
9
Erscheinungsjahr:
2005
Anzahl der Seiten:
6
Notiz zur Publikation:
Authors: Edwin Lughofer, Eyke Hüllermeier, Erich Peter Klement