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Neural Learning of Chaotic Dynamics: The Error Propagation Algorithm

dc.contributor.authorBakker, Rembrandten_US
dc.contributor.authorSchouten, Jaap C.en_US
dc.contributor.authorBleek, Cor M. van denen_US
dc.contributor.authorGiles, C. Leeen_US
dc.description.abstractAn 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)en_US
dc.format.extent2078726 bytes
dc.relation.ispartofseriesUM Computer Science Department; CS-TR-3843en_US
dc.relation.ispartofseriesUMIACS; UMIACS-TR-97-77en_US
dc.titleNeural Learning of Chaotic Dynamics: The Error Propagation Algorithmen_US
dc.typeTechnical Reporten_US
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_US
dc.relation.isAvailableAtUniversity of Maryland (College Park, Md.)en_US
dc.relation.isAvailableAtTech Reports in Computer Science and Engineeringen_US
dc.relation.isAvailableAtUMIACS Technical Reportsen_US

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