Abstract
A singular-value decomposition leads to a set of statistically independent variables which are used in the Grassberger-Procaccia algorithm to calculate the correlation dimension of an attractor from a scalar time series. This combination alleviates some of the difficulties associated with each technique when used alone, and can significantly reduce the computational cost of estimating correlation dimensions from a time series.
- Received 14 January 1988
DOI:https://doi.org/10.1103/PhysRevA.38.3017
©1988 American Physical Society