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Neighbourhood detection and indentification of spatio-temporal dynamical systems using a coarse-to-fine approach

Guo, L.Z., Mei, S.S. and Billings, S.A. (2002) Neighbourhood detection and indentification of spatio-temporal dynamical systems using a coarse-to-fine approach. Research Report. ACSE Research Report no. 828 . Automatic Control and Systems Engineering, University of Sheffield

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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. Three examples are used to demonstrate the application of the new approach.

Item Type: Monograph (Research Report)
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.
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) > ACSE Research Reports
Depositing User: Miss Anthea Tucker
Date Deposited: 11 Sep 2012 09:22
Last Modified: 08 Jun 2014 14:15
Status: Published
Publisher: Automatic Control and Systems Engineering, University of Sheffield
URI: http://eprints.whiterose.ac.uk/id/eprint/74486

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