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
Krishnanathan K
(2015)
Computational system identification of continuous-time nonlinear systems using approximate Bayesian computation
in International Journal of Systems Science
Billings S
(2015)
Identification of nonlinear systems with non-persistent excitation using an iterative forward orthogonal least squares regression Algorithm
in International Journal of Modelling, Identification and Control
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
Sarrigiannis PG
(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
Guo Y
(2014)
An iterative orthogonal forward regression algorithm
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
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
(2011)
IDENTIFICATION OF n-STATE SPATIO-TEMPORAL DYNAMICAL SYSTEMS USING A POLYNOMIAL MODEL
in International Journal of Bifurcation and Chaos
Wei HL
(2009)
Lattice dynamical wavelet neural networks implemented using particle swarm optimization for spatio-temporal system identification.
in IEEE transactions on neural networks
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