Spatio-Temporal Systems Estimation, Modelling and Analysis
Lead Research Organisation:
University of Sheffield
Department Name: Automatic Control and Systems Eng
Abstract
Systems where the dynamics at any point depends upon the dynamics back in time and the dynamics at neighbouring spatial locations are ubiquitous. These space-time or spatio-temporal systems represent an enormous class of highly complex dynamical systems that have been largely ignored in studies to date. It is the spatial interactions over a neighbourhood of influence that together with the temporal dynamics combine to produce evolving patterns of complex interacting behaviours. Real life examples of spatio-temporal systems are easy to find and range from neuro-imaging applications to stem cells and reaction diffusion systems. The main objective of this research study will be to derive generic systems identification and parameter estimation based procedures to identify models of these complex systems, to develop model validation methods for this model class, to investigate characterisation based on invariant dynamic measures, and to apply the results to a range of both simulated and real spatio-temporal systems.
Organisations
Publications
Nehmzow, U
(2010)
'Do empirical models of robot environment interaction have a meaning
Krishnanathan K
(2012)
A data-driven framework for identifying nonlinear dynamic models of genetic parts.
in ACS synthetic biology
Zheng Y
(2010)
A dynamic model of neurovascular coupling: implications for blood vessel dilation and constriction.
in NeuroImage
Zhao Y
(2012)
A New Adaptive Fast Cellular Automaton Neighborhood Detection and Rule Identification Algorithm.
in IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society
Zhao Y
(2013)
A new NARX-based Granger linear and nonlinear casual influence detection method with applications to EEG data.
in Journal of neuroscience methods
He F
(2014)
A nonlinear generalization of spectral Granger causality.
in IEEE transactions on bio-medical engineering
Guo Y
(2013)
A parametric frequency response method for non-linear time-varying systems
in International Journal of Systems Science
Zhao Y
(2013)
A parametric method to measure time-varying linear and nonlinear causality with applications to EEG data.
in IEEE transactions on bio-medical engineering
| Description | Developed new theories, methods and algorithms to analyze, model, simulate and control complex spatio-temporal systems |
| Exploitation Route | Applications range from modelling crystal growth, to space weather forecasting and flow control |
| Sectors | Agriculture, Food and Drink,Chemicals,Communities and Social Services/Policy,Environment,Healthcare |
| Description | By medics in treating epilepsy and deep brain stimulation. Space weather forecasting models - Real time forecast of the >800 keV electron flux at geosynchronous orbit |
| First Year Of Impact | 2015 |
| Sector | Chemicals,Environment,Healthcare,Pharmaceuticals and Medical Biotechnology |
| Impact Types | Societal |
| Description | Program Grant |
| Amount | $900,000 (USD) |
| Funding ID | RGP0001/2012 |
| Organisation | Human Frontier Science Program (HFSP) |
| Sector | Charity/Non Profit |
| Country | France |
| Start | 06/2012 |
| End | 06/2015 |