## 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. To decide when to stop training, a criterion is monitored during training to select the model that has a chaotic attractor most similar to the real system's 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.

Original language | English (US) |
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Title of host publication | IEEE World Congress on Computational Intelligence |

Editors | Anon |

Publisher | IEEE |

Pages | 2483-2488 |

Number of pages | 6 |

Volume | 3 |

State | Published - 1998 |

Event | Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3) - Anchorage, AK, USA Duration: May 4 1998 → May 9 1998 |

### Other

Other | Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3) |
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City | Anchorage, AK, USA |

Period | 5/4/98 → 5/9/98 |

## All Science Journal Classification (ASJC) codes

- Software