Robust learning of chaotic attractors

Rembrandt Bakker, Jaap C. Sochouten, Marc Olivier Coppens, Floris Takens, C. Lee Giles, Cor M. Van Den Bleek

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

6 Scopus citations


A fundamental problem with the modeling of chaotic time series data is that minimizing short-term prediction errors does not guarantee a match between the reconstructed attractors of model and experiments. We introduce a modeling paradigm that simultaneously learns to short-term predict and to locate the outlines of the attractor by a new way of nonlinear principal component analysis. Closed-loop predictions are constrained to stay within these outlines, to prevent divergence from the attractor. Learning is exceptionally fast: parameter estimation for the 1000 sample laser data from the 1991 Santa Fe time series competition took less than a minute on a 166 MHz Pentium PC.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 12 - Proceedings of the 1999 Conference, NIPS 1999
PublisherNeural information processing systems foundation
Number of pages7
ISBN (Print)0262194503, 9780262194501
StatePublished - 2000
Event13th Annual Neural Information Processing Systems Conference, NIPS 1999 - Denver, CO, United States
Duration: Nov 29 1999Dec 4 1999

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258


Other13th Annual Neural Information Processing Systems Conference, NIPS 1999
Country/TerritoryUnited States
CityDenver, CO

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing


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