Detecting chaos with neural networks

Research output: Contribution to journalArticle

Authors

Colleges, School and Institutes

External organisations

  • University of Oxford

Abstract

Predictions of the future values of a time series can be used to try to distinguish chaotic from noisy signals. We show that neurally inspired networks provide a powerful tool for this task, in that they can reliably distinguish between predictable, chaotic and noisy records. We have used these neural networks to analyse the 'standard' time series of measles and chickenpox cases in New York City. Applied to these real time series these new methods perform as well as a recently developed technique. We also employed feedforward and recurrent networks to analyse several sets of artificially generated data and found that they exhibit advantages over other recent techniques. These networks were able to generalize the rules governing the generation of the time series from very few data points and could also forecast series generated by non-stationary dynamics.

Details

Original languageEnglish
Pages (from-to)82-86
Number of pages5
JournalProceedings of the Royal Society B: Biological Sciences
Volume242
Issue number1304
Publication statusPublished - 1 Jan 1990