Parametric Fuzzy Modelling Framework for Complex Data-Inherent Structures

• Main Authors: Hempel, Arne-Jens, Bocklisch, Steffen F.
• Other Authors: TU Chemnitz, Fakultät für Elektrotechnik und Informationstechnik
• Format: Others
• Language: English
• Published: Universitätsbibliothek Chemnitz 2009
• Subjects: ddc:620; Fuzzy-Logik; Klassifikation; Modelllernen; Multivariate Analyse; Multivariate Daten
• Online Access: http://nbn-resolving.de/urn:nbn:de:bsz:ch1-200901487; http://nbn-resolving.de/urn:nbn:de:bsz:ch1-200901487; http://www.qucosa.de/fileadmin/data/qucosa/documents/5870/data/ArneJensHempel_IFSAEUSFLAT2009.pdf; http://www.qucosa.de/fileadmin/data/qucosa/documents/5870/20090148.txt

The present article dedicates itself to fuzzy modelling of data-inherent structures. In particular two main points are dealt with: the introduction of a fuzzy modelling framework and the elaboration of an automated, data-driven design strategy to model complex data-inherent structures within this framework. The innovation concerning the modelling framework lies in the fact that it is consistently built around a single, generic type of parametrical and convex membership function. In the first part of the article this essential building block will be defined and its assets and shortcomings will be discussed. The novelty regarding the automated, data-driven design strategy consist in the conservation of the modelling framework when modelling complex (nonconvex) data-inherent structures. Instead of applying current clustering methods the design strategy uses the inverse of the data structure in order to created a fuzzy model solely based on convex membership functions. Throughout the article the whole model design process is illustrated, section by section, with the help of an academic example.