From data-to dynamics: predicting chaotic time series by hierarchical Bayesian neural nets

A hierarchical Bayesian algorithm was used to make predictions of chaotic time series data generated by the Rossler system which is a continuous dynamical system. The scheme infers a nonlinear dynamical system model using feedforward neural nets. The most difficult task, estimation of the embedding...

Ausführliche Beschreibung

Bibliographische Detailangaben

Hauptverfasser:	Matsumoto, T., Hamagishi, H., Sugi, J., Saito, M.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Bayesian methods Chaos Distributed computing Inverse problems Linearity Neural networks Noise generators Parameter estimation Prediction algorithms Uncertainty
Online-Zugang:	Volltext bestellen
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