System Identification and Model Validation for Spatio-Temporal Dynamical Systems

Lead Research Organisation: University of Sheffield
Department Name: Automatic Control and Systems Eng

Abstract

Spatio-temporal systems are systems that evolve over both space and time. Until recently the lack of tools for analysing spatio-temporal systems has not been a limitation since most experiments produced purely temporal information in the form of measurements at a specific location or site. But there are many important systems where space and time are essential for explaining the observed phenomena. The main objective of this research study will be to investigate the identification of models of spatio-temporal systems where the cell entries can be either continuous or binary variables and to study the validation and other properties of this important class of nonlinear systems.

Publications

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Boynton R (2015) Online NARMAX model for electron fluxes at GEO in Annales Geophysicae

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Friederich U (2016) Fly Photoreceptors Encode Phase Congruency. in PloS one

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GUO Y (2011) IDENTIFICATION OF n-STATE SPATIO-TEMPORAL DYNAMICAL SYSTEMS USING A POLYNOMIAL MODEL in International Journal of Bifurcation and Chaos

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Guo Y (2014) An iterative orthogonal forward regression algorithm in International Journal of Systems Science

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Pan Y (2008) Neighborhood detection for the identification of spatiotemporal systems. in IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society

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Zhao Y (2009) Cellular automata modelling of dendritic crystal growth based on Moore and von Neumann neighbourhoods in International Journal of Modelling, Identification and Control