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    Neural Learning of Chaotic Dynamics: The Error Propagation Algorithm

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    No. of downloads: 217

    Date
    1998-10-15
    Author
    Bakker, Rembrandt
    Schouten, Jaap C.
    Bleek, Cor M. van den
    Giles, C. Lee
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    Abstract
    An algorithm is introduced that trains a neural network to identify chaotic dynamics from a single measured time-series. The algorithm has four special features: 1. The state of the system is extracted from the time-series using delays, followed by weighted Principal Component Analysis (PCA) data reduction. 2. The prediction model consists of both a linear model and a Multi- Layer-Perceptron (MLP). 3. The effective prediction horizon during training is user-adjustable due to error propagation: prediction errors are partially propagated to the next time step. 4. A criterion is monitored during training to select the model that as a chaotic attractor is most similar to the real system attractor. The algorithm is applied to laser data from the Santa Fe time-series competition (set A). The resulting model is not only useful for short-term predictions but it also generates time-series with similar chaotic characteristics as the measured data. _Also cross-referenced as UMIACS-TR-97-77)
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    http://hdl.handle.net/1903/922
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    • Technical Reports of the Computer Science Department

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