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
He F
(2016)
Nonlinear interactions in the thalamocortical loop in essential tremor: A model-based frequency domain analysis.
in Neuroscience
GUO Y
(2011)
IDENTIFICATION OF n-STATE SPATIO-TEMPORAL DYNAMICAL SYSTEMS USING A POLYNOMIAL MODEL
in International Journal of Bifurcation and Chaos
Guo Y
(2015)
Identification of continuous-time models for nonlinear dynamic systems from discrete data
in International Journal of Systems Science
Guo Y
(2016)
Ultra-Orthogonal Forward Regression Algorithms for the Identification of Non-Linear Dynamic Systems
in Neurocomputing
Guo Y
(2016)
A New Efficient System Identification Method for Nonlinear Multiple Degree-of-Freedom Structural Dynamic Systems
in Journal of Computational and Nonlinear Dynamics
Guo Y
(2014)
An iterative orthogonal forward regression algorithm
in International Journal of Systems Science
Guo L
(2015)
Approximate observability of infinite dimensional bilinear systems using a Volterra series expansion
in Systems & Control Letters
Guo L
(2007)
State-Space Reconstruction and Spatio-Temporal Prediction of Lattice Dynamical Systems
in IEEE Transactions on Automatic Control
Friederich U
(2016)
Fly Photoreceptors Encode Phase Congruency.
in PloS one
Boynton R
(2015)
Online NARMAX model for electron fluxes at GEO
in Annales Geophysicae