Segmentation and identification of drifting dynamical systems

J. Kohlmorgen, K. R. Mueller, K. Pawelzik

Research output: Contribution to conferencePaper

10 Citations (Scopus)

Abstract

A method for the analysis of nonstationary time series with multiple operating modes is presented. In particular, it is possible to detect and to model a switching of the dynamics and also a less abrupt, time consuming drift from one mode to another. This is achieved by an unsupervised algorithm that segments the data according to inherent modes, and a subsequent search through the space of possible drifts. An application to physiological wake/sleep data demonstrates that analysis and modeling of real-world time series can be improved when the drift paradigm is taken into account. In the case of wake/sleep data, we hope to gain more insight into the physiological processes that are involved in the transition from wake to sleep.

Original languageEnglish
Pages326-335
Number of pages10
Publication statusPublished - 1997
Externally publishedYes
EventProceedings of the 1997 7th IEEE Workshop on Neural Networks for Signal Processing, NNSP'97 - Amelia Island, FL, USA
Duration: 1997 Sep 241997 Sep 26

Other

OtherProceedings of the 1997 7th IEEE Workshop on Neural Networks for Signal Processing, NNSP'97
CityAmelia Island, FL, USA
Period97/9/2497/9/26

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

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    Kohlmorgen, J., Mueller, K. R., & Pawelzik, K. (1997). Segmentation and identification of drifting dynamical systems. 326-335. Paper presented at Proceedings of the 1997 7th IEEE Workshop on Neural Networks for Signal Processing, NNSP'97, Amelia Island, FL, USA, .