Predicting Chaotic Time Series by Boosted Recurrent Neural Networks

This paper discusses the use of a recent boosting algorithm for recurrent neural networks as a tool to model nonlinear dynamical systems. It combines a large number of RNNs, each of which is generated by training on a different set of examples. This algorithm is based on the boosting algorithm where...

Ausführliche Beschreibung

Bibliographische Detailangaben

Hauptverfasser:	Assaad, Mohammad, Boné, Romuald, Cardot, Hubert
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Applied sciences Artificial intelligence Boosting Chaotic time series Computer science control theory systems Connectionism. Neural networks Exact sciences and technology Recurrent neural networks Time series forecasting
Online-Zugang:	Volltext
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