Analysis of Switching Dynamics with Competing Neural Networks

Klaus-Robert MÜLLER; Jens KOHLMORGEN; Klaus PAWELZIK

Analysis of Switching Dynamics with Competing Neural Networks

Klaus-Robert MÜLLER, Jens KOHLMORGEN, Klaus PAWELZIK

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We present a framework for the unsupervised segmentation of time series. It applies to non-stationary signals originating from different dynamical systems which alternate in time, a phenomenon which appears in many natural systems. In our approach, predictors compete for data points of a given time series. We combine competition and evolutionary inertia to a learning rule. Under this learning rule the system evolves such that the predictors, which finally survive, unambiguously identify the underlying processes. The segmentation achieved by this method is very precise and transients are included, a fact, which makes our approach promising for future applications.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E78-A No.10 pp.1306-1315

Publication Date: 1995/10/25

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Type of Manuscript: Special Section PAPER (Special Section on Nonlinear Theory and Its Applications)

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Klaus-Robert MÜLLER, Jens KOHLMORGEN, Klaus PAWELZIK, "Analysis of Switching Dynamics with Competing Neural Networks" in IEICE TRANSACTIONS on Fundamentals, vol. E78-A, no. 10, pp. 1306-1315, October 1995, doi: .
Abstract: We present a framework for the unsupervised segmentation of time series. It applies to non-stationary signals originating from different dynamical systems which alternate in time, a phenomenon which appears in many natural systems. In our approach, predictors compete for data points of a given time series. We combine competition and evolutionary inertia to a learning rule. Under this learning rule the system evolves such that the predictors, which finally survive, unambiguously identify the underlying processes. The segmentation achieved by this method is very precise and transients are included, a fact, which makes our approach promising for future applications.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e78-a_10_1306/_p

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@ARTICLE{e78-a_10_1306,
author={Klaus-Robert MÜLLER, Jens KOHLMORGEN, Klaus PAWELZIK, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Analysis of Switching Dynamics with Competing Neural Networks},
year={1995},
volume={E78-A},
number={10},
pages={1306-1315},
abstract={We present a framework for the unsupervised segmentation of time series. It applies to non-stationary signals originating from different dynamical systems which alternate in time, a phenomenon which appears in many natural systems. In our approach, predictors compete for data points of a given time series. We combine competition and evolutionary inertia to a learning rule. Under this learning rule the system evolves such that the predictors, which finally survive, unambiguously identify the underlying processes. The segmentation achieved by this method is very precise and transients are included, a fact, which makes our approach promising for future applications.},
keywords={},
doi={},
ISSN={},
month={October},}

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TY - JOUR
TI - Analysis of Switching Dynamics with Competing Neural Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1306
EP - 1315
AU - Klaus-Robert MÜLLER
AU - Jens KOHLMORGEN
AU - Klaus PAWELZIK
PY - 1995
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E78-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 1995
AB - We present a framework for the unsupervised segmentation of time series. It applies to non-stationary signals originating from different dynamical systems which alternate in time, a phenomenon which appears in many natural systems. In our approach, predictors compete for data points of a given time series. We combine competition and evolutionary inertia to a learning rule. Under this learning rule the system evolves such that the predictors, which finally survive, unambiguously identify the underlying processes. The segmentation achieved by this method is very precise and transients are included, a fact, which makes our approach promising for future applications.
ER -