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Merkmalsbildung und lernfähige Klassifikation von Zeitreihen

 
: Wisotzki, C.; Wysotzki, F.

Deutsche Forschungsvereinigung für Meß-, Regelungs- und Systemtechnik e.V.:
Jahrestagung 1993 der Deutschen Forschungsvereinigung für Meß-, Regelungs- und Systemtechnik e.V
1993 (Forschungsbericht - Deutsche Forschungsvereinigung für Meß- Regelungs- und Systemtechnik e.V.)
ISSN: 0944-694X
Mustererkennung, Signal-Verarbeitung, Fuzzy-Systeme <1993>
German
Conference Paper
Fraunhofer IITB, Außenstelle Prozessoptimierung (EPO); 1997
artificial intelligence; function approximation; Funktionsapproximation; Künstliche Intelligenz; learning system; lernendes System; neuronal network; neuronales Netzwerk

Abstract
In the paper classification methods for time series with a high number of measured values are developed. The measurements can be noised. Therefore, the use of classification methods is not possible without former treatment. In order to form and reduce features, the time series are approximated by spline functions where the number and the location of the joints are defined by the method. If the measurements are given over one and the same time interval, the splines can be classified immediately. Otherwise, the splines can be transformed into strings. For classifying strings, a distance comparing strings of different lengths is defined with the help of the so-called compatibility graph.

: http://publica.fraunhofer.de/documents/PX-23460.html