Smoothing data with Local Instabilities for the Identification of Chaotic Systems.
Aguirre, L.A., Mendes, E.M. and Billings, S.A.
(1994)
Smoothing data with Local Instabilities for the Identification of Chaotic Systems.
Research Report.
ACSE Research Report 502
.
Department of Automatic Control and Systems Engineering
Abstract
Chaotic systems are characterised by local divergence of nearby orbits in state space. This provokes sensitive dependence on initial conditions and in turn drastically limits the accuracy of long term predictions. This has important implications in the filtering of data generated by such systems. In the present paper, the use of global smoothers for chaotic data is investigated. The ultimate objective is to be able to identify dynamically valid models from smoothed data when the identification from the original noisy data has completely failed. The objective is to produce identified models which faithfully reproduce the dynamical invariants of the original system such as the geometry of the attractors in state space, the largest Lyapunov exponent, fractal dimensions and Poincare sections. Numerical examples are included which illustrate the main points of the paper.
Metadata
Item Type:
Monograph
Authors/Creators:
Aguirre, L.A.
Mendes, E.M.
Billings, S.A.
Copyright, Publisher and Additional Information:
The Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instances.
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