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Analysis of nonstationary time series by mixtures of self-organizing predictors

Content Provider	IEEE Xplore Digital Library
Author	Kohlmorgen, J. Lemm, S. Ratsch, G. Muller, K.-R.
Copyright Year	2000
Description	Author affiliation: Inst. for Comput. Archit. & Software Technol., German Nat. Res. Center for Inf. Technol., Berlin, Germany (Kohlmorgen, J.)
Abstract	Presents a method for the analysis of time series from drifting or switching dynamics. In an extension to existing approaches that identify switches or drifts between stationary dynamical modes, the method allows one to analyze even continuously varying dynamics and can identify mixtures of more than two dynamical modes. The architecture is based on a mixture of self-organizing Nadaraya-Watson kernel estimators. The mixture model is trained by barrier optimization, a technique for constrained optimization problems. We apply the proposed method to artificially generated data and EEG recordings from the wake/sleep transition.
Starting Page	85
Ending Page	94
File Size	579291
Page Count	10
File Format	PDF
ISBN	0780362780
ISSN	10893555
DOI	10.1109/NNSP.2000.889365
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-12-11
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Time series analysis Predictive models Electroencephalography Kernel Constraint optimization Information technology Computer architecture Software Electronic mail Switches
Content Type	Text
Resource Type	Article

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