Neural learning of chaotic dynamics: The error propagation algorithm

Rembrandt Bakker, Jaap C. Schouten, Cor M. van den Bleek, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

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 languageEnglish (US)
Title of host publicationIEEE World Congress on Computational Intelligence
Editors Anon
PublisherIEEE
Pages2483-2488
Number of pages6
Volume3
StatePublished - 1998
EventProceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3) - Anchorage, AK, USA
Duration: May 4 1998May 9 1998

Other

OtherProceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3)
CityAnchorage, AK, USA
Period5/4/985/9/98

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Software

Cite this

Bakker, R., Schouten, J. C., van den Bleek, C. M., & Giles, C. L. (1998). Neural learning of chaotic dynamics: The error propagation algorithm. In Anon (Ed.), IEEE World Congress on Computational Intelligence (Vol. 3, pp. 2483-2488). IEEE.