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.
Organisations
Publications
Guo L
(2015)
Approximate observability of infinite dimensional bilinear systems using a Volterra series expansion
in Systems & Control Letters
Balikhin MA
(2016)
Comparative analysis of NOAA REFM and SNB3GEO tools for the forecast of the fluxes of high-energy electrons at GEO.
in Space weather : the international journal of research & applications
Friederich U
(2016)
Fly Photoreceptors Encode Phase Congruency.
in PloS one
He F
(2016)
Nonlinear interactions in the thalamocortical loop in essential tremor: A model-based frequency domain analysis.
in Neuroscience
Guo Y
(2016)
Ultra-Orthogonal Forward Regression Algorithms for the Identification of Non-Linear Dynamic Systems
in Neurocomputing
Zhang B
(2015)
Identification of continuous-time nonlinear systems: The nonlinear difference equation with moving average noise (NDEMA) framework
in Mechanical Systems and Signal Processing
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
(2015)
Identification of continuous-time models for nonlinear dynamic systems from discrete data
in International Journal of Systems Science
Zhu Q
(2013)
Review of rational (total) nonlinear dynamic system modelling, identification, and control
in International Journal of Systems Science
Li Y
(2015)
Identification of nonlinear time-varying systems using an online sliding-window and common model structure selection (CMSS) approach with applications to EEG
in International Journal of Systems Science