Identification of non-linear stochastic spatiotemporal dynamical systems
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 2069-2083 |
| Journal / Publication | IET Control Theory and Applications |
| Volume | 7 |
| Issue number | 17 |
| Online published | 1 Nov 2013 |
| Publication status | Published - Nov 2013 |
| Externally published | Yes |
Link(s)
Abstract
A systematic identification method for non-linear stochastic spatiotemproal (SST) systems described by non-linear stochastic partial differential equations (SPDEs) is investigated in this study based on pointwise observation data. A theoretical framework for a semi-finite element model approximating to an infinite-dimensional system is established, and several fundamental issues are discussed including the approximation error between the underlying infinite-dimensional dynamics and the model to be identified, and its rationality etc. Based on the proposed theoretical framework, a general identification method with irregular observation data is provided. These results not only provide an effective method for the identification of non-linear SST systems using measurement data (both offline and online), but also demonstrate a potential solution for the analysis, design and control of non-linear SST systems from a numerical point of view.
Research Area(s)
- control system synthesis, identification, nonlinear differential equations, nonlinear dynamical systems, partial differential equations, stochastic systems, nonlinear stochastic spatio-temporal dynamical system, systematic identification method, SST systems, nonlinear stochastic partial differential equations, SPDE, pointwise observation data, semifinite element model, infinite-dimensional dynamic system, approximation error, general identification method, irregular observation data, measurement data, PARTIAL-DIFFERENTIAL-EQUATIONS, FINITE-ELEMENT METHODS, MODELS, APPROXIMATION, PREDICTION, SELECTION
Citation Format(s)
In: IET Control Theory and Applications, Vol. 7, No. 17, 11.2013, p. 2069-2083.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
