Detecting chaos with neural networks
Research output: Contribution to journal › Article
Colleges, School and Institutes
- University of Oxford
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.
|Number of pages||5|
|Journal||Proceedings of the Royal Society B: Biological Sciences|
|Publication status||Published - 1 Jan 1990|