Annealed Competition of Experts for a Segmentation and Classification of Switching Dynamics

Klaus Pawelzik, Jens Kohlmorgen, Klaus Robert Müller

Research output: Contribution to journalArticlepeer-review

62 Citations (Scopus)

Abstract

We present a method for the unsupervised segmentation of data streams originating from different unknown sources that alternate in time. We use an architecture consisting of competing neural networks. Memory is included to resolve ambiguities of input-output relations. To obtain maximal specialization, the competition is adiabatically increased during training. Our method achieves almost perfect identification and segmentation in the case of switching chaotic dynamics where input manifolds overlap and input-output relations are ambiguous. Only a small dataset is needed for the training procedure. Applications to time series from complex systems demonstrate the potential relevance of our approach for time series analysis and short-term prediction.

Original languageEnglish
Pages (from-to)340-356
Number of pages17
JournalNeural Computation
Volume8
Issue number2
DOIs
Publication statusPublished - 1996 Feb 15
Externally publishedYes

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Cognitive Neuroscience

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