Paper
28 October 1994 Filtering chaotic time series
Michael E. Davies
Author Affiliations +
Abstract
We consider the relationship between modelling dynamics with a nonlinear function approximation and the application of a noise reduction algorithm. Filtering the data with such an algorithm is then shown to provide a better deterministic model for the data than an ordinary least squares estimate.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Michael E. Davies "Filtering chaotic time series", Proc. SPIE 2296, Advanced Signal Processing: Algorithms, Architectures, and Implementations V, (28 October 1994); https://doi.org/10.1117/12.190842
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KEYWORDS
Denoising

Data modeling

Nonlinear filtering

Systems modeling

Algorithm development

Dynamical systems

Fractal analysis

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