Original scientific paper
https://doi.org/10.2498/cit.2003.01.04
Detecting Noise in Chaotic Signals through Principal Component Matrix Transformation
Božidar Vojnović
Ivan Michieli
Abstract
We study the reconstruction of continuous chaotic attractors from noisy time-series. A method of delays and principal component eigenbasis (defined by singular vectors) is used for state vectors reconstruction. We introduce a simple measure of trajectory vectors directional distribution for chosen principal component subspace, based on nonlinear transformation of principal component matrix. The value of such defined measure is dependent on the amount of noise in the data. For isotropically distributed noise (or close to isotropic), that allows us to set up window width boundaries for acceptable attractor reconstruction as a function of noise content in the data.
Keywords
Hrčak ID:
44766
URI
Publication date:
30.3.2003.
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