Analysis of Switching Dynamics with Competing Neural Networks

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概要

• 論文の詳細を見る

• 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.

• 社団法人電子情報通信学会の論文
• 1995-10-25

著者

• Mueller K‐r

  Department Of Mathematical Engineering And Information Physics The University Of Tokyo

• Klaus-robert Muller

  Department Of Mathematical Engineering And Information Physics The University Of Tokyo

• Kohlmorgen Jens

  GMD FIRST

• Pawelzik Klaus

  Institut fur Theoretische Physik, Universitat Frankfurt

• Pawelzik Klaus

  Institut Fur Theoretische Physik Universitat Frankfurt

関連論文

• Analysis of Switching Dynamics with Competing Neural Networks

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