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

نویسنده

  • Klaus Pawelzik
چکیده

We present a method for the unsupervised segmentation of data streams originating from diierent unknown sources which alternate in time. We use an architecture consisting of competing neural networks. Memory is included in order to resolve ambiguities of input-output relations. In order to obtain maximal specialization , the competition is adiabatically increased during training. Our method achieves almost perfect identiication 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 proceedure. Applications to time series from complex systems demonstrate the potential relevance of our approach for time series analysis and short-term prediction.

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تاریخ انتشار 1996