Neural Learning of Chaotic Dynamics: The Error Propagation Algorithm
| dc.contributor.author | Bakker, Rembrandt | en_US |
| dc.contributor.author | Schouten, Jaap C. | en_US |
| dc.contributor.author | Bleek, Cor M. van den | en_US |
| dc.contributor.author | Giles, C. Lee | en_US |
| dc.date.accessioned | 2004-05-31T22:48:25Z | |
| dc.date.available | 2004-05-31T22:48:25Z | |
| dc.date.created | 1997-10 | en_US |
| dc.date.issued | 1998-10-15 | en_US |
| dc.description.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) | en_US |
| dc.format.extent | 2078726 bytes | |
| dc.format.mimetype | application/postscript | |
| dc.identifier.uri | http://hdl.handle.net/1903/922 | |
| dc.language.iso | en_US | |
| dc.relation.isAvailableAt | Digital Repository at the University of Maryland | en_US |
| dc.relation.isAvailableAt | University of Maryland (College Park, Md.) | en_US |
| dc.relation.isAvailableAt | Tech Reports in Computer Science and Engineering | en_US |
| dc.relation.isAvailableAt | UMIACS Technical Reports | en_US |
| dc.relation.ispartofseries | UM Computer Science Department; CS-TR-3843 | en_US |
| dc.relation.ispartofseries | UMIACS; UMIACS-TR-97-77 | en_US |
| dc.title | Neural Learning of Chaotic Dynamics: The Error Propagation Algorithm | en_US |
| dc.type | Technical Report | en_US |
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