NDLI: Premise parameter estimation and adaptation in fuzzy systems with open-loop clustering methods

Content Provider	IEEE Xplore Digital Library
Author	Lughofer, E. Klement, E.P.
Copyright Year	2004
Description	Author affiliation: Fuzzy Logic Laboratorium, Johannes Kepler Univ., Linz, Austria (Lughofer, E.; Klement, E.P.)
Abstract	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 up to 5-dimensional fuzzy models. Additionally; a detailed analysis of the method is given.
Starting Page	499
Ending Page	504
File Size	872076
Page Count	6
File Format	PDF
ISBN	0780383532
ISSN	10987584
DOI	10.1109/FUZZY.2004.1375781
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-07-25
Publisher Place	Hungary
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Parameter estimation Fuzzy systems Clustering methods Clustering algorithms Unsupervised learning Image sensors Sensor phenomena and characterization State estimation Takagi-Sugeno model Inference algorithms
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Theoretical Computer Science Software

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