Learning algorithm for modeling complex spatial dynamics
- 16 October 1989
- journal article
- research article
- Published by American Physical Society (APS) in Physical Review Letters
- Vol. 63 (16) , 1735-1738
- https://doi.org/10.1103/physrevlett.63.1735
Abstract
A learning algorithm is developed to build a dynamical model from complex spatial data consisting of discrete values on a lattice evolving in time. The resulting dynamical system is a cellular automaton, and may be used for forecasting or for regenerating global spatial patterns. Part of the learning algorithm is a novel application of the genetic algorithm, originally developed in the field of machine learning. We outline extensions of this method to construct models for spatial dynamics that have continuous variables as well.Keywords
This publication has 8 references indexed in Scilit:
- Fast filter transform for image processingPublished by Elsevier ,2004
- Information transport in spatiotemporal systemsPhysical Review Letters, 1988
- Spatiotemporal Intermittency in Rayleigh-Bénard ConvectionPhysical Review Letters, 1988
- Predicting chaotic time seriesPhysical Review Letters, 1987
- Construction of Differential Equations from Experimental DataZeitschrift für Naturforschung A, 1987
- Dendritic and Fractal Patterns in Electrolytic Metal DepositsPhysical Review Letters, 1986
- Independent coordinates for strange attractors from mutual informationPhysical Review A, 1986
- Pattern Competition Leads to ChaosPhysical Review Letters, 1984