Neighbourhood Detection and Identification of Spatio-Temporal Dynamical Systems Using a Course-To-Fine Approach
Guo, L.Z., Mei, S.S. and Billings, S.A.
(2002)
Neighbourhood Detection and Identification of Spatio-Temporal Dynamical Systems Using a Course-To-Fine Approach.
Research Report.
ACSE Research Report 828
.
Department of Automatic Control and Systems Engineering
Abstract
A novel approach to the determination of the neighbourhood and the identification of spatio-temporal dynamical systems is investigated. It is shown that thresholding to convert the pattern to a binary pattern and then applying cellular automata (CA) neighbourhood detection methods can provide an initial estimate of the neighbourhood. A coupled map lattice model can then be identified using the CA detected neighbourhood as the initial conditions. This provides a coarse-to-fine approach for neighbourhood detection and identification of coupled map lattice models. Two examples are used to demonstrate the application of the new approach.
Metadata
Item Type:
Monograph
Authors/Creators:
Guo, L.Z.
Mei, S.S.
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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