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
Zhu Q
(2013)
Review of rational (total) nonlinear dynamic system modelling, identification, and control
in International Journal of Systems Science
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
Krishnanathan K
(2015)
Computational system identification of continuous-time nonlinear systems using approximate Bayesian computation
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
Guo Y
(2014)
An iterative orthogonal forward regression algorithm
in International Journal of Systems Science
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
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)
Ultra-Orthogonal Forward Regression Algorithms for the Identification of Non-Linear Dynamic Systems
in Neurocomputing
He F
(2016)
Nonlinear interactions in the thalamocortical loop in essential tremor: A model-based frequency domain analysis.
in Neuroscience
Friederich U
(2016)
Fly Photoreceptors Encode Phase Congruency.
in PloS one