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
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
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
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
Krishnanathan K
(2015)
Computational system identification of continuous-time nonlinear systems using approximate Bayesian computation
in International Journal of Systems Science
Sarrigiannis P
(2015)
Direct Functional Connectivity between the Thalamus (Vim) and the Contralateral Motor Cortex: Just a Single Case Observation or a Common Pathway in the Human Brain?
in Brain Stimulation
Friederich U
(2016)
Fly Photoreceptors Encode Phase Congruency.
in PloS one
He F
(2013)
Identification and frequency domain analysis of non-stationary and nonlinear systems using time-varying NARMAX models
in International Journal of Systems Science
Guo Y
(2015)
Identification of continuous-time models for nonlinear dynamic systems from discrete data
in International Journal of Systems Science