Erich Klement, Edwin Lughofer,
"Premise Parameter Estimation and Adaptation in Fuzzy Systems with Open-Loop Clustering Methods"
: Proceedings FUZZ-IEEE 2004,, 7-2004
Original Titel:
Premise Parameter Estimation and Adaptation in Fuzzy Systems with Open-Loop Clustering Methods
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings FUZZ-IEEE 2004,
Original Kurzfassung:
Clustering algorithms as unsupervised learning techniques
are of fundamental importance in order to group any kind of
recorded measurement data (in form of images, signals or physical
values from sensors) into separate regions, also called clusters.
This grouping is not only applied whenever a classification of
feature vectors representing special attributes of the data set is
required, but also in the case of approximating arbitrary
relationships which possess an intense local (in the case of
static processes) or time-variant (in the case of dynamic
processes) behavior and therefore cannot be described with one
closed analytical formula over the whole domain. In this paper
first open-loop clustering methods are described, i.e. clustering
methods which are able to adapt former generated clusters
pointwise. Afterwards, a new approach for estimating and updating
nonlinear parameters in Takagi-Sugeno fuzzy inference systems,
i.e. premise parameters in the rules' antecedents, by applying
open-loop clustering algorithms is stated together with the impact
on the bias error and training time for 2-dimensional fuzzy
models. Additionally, a detailed analysis of the method is given.
Forschungs-