Open AccessIdentification of non‐linear stochastic spatiotemporal dynamical systems
Author(s) -
Ning Hanwen,
Jing Xingjian,
Cheng Li
Publication year - 2013
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2013.0150
Subject(s) - pointwise , identification (biology) , dynamical systems theory , mathematics , partial differential equation , system identification , linear system , computer science , parameter identification problem , linear dynamical system , control theory (sociology) , mathematical optimization , data modeling , control (management) , mathematical analysis , artificial intelligence , botany , physics , quantum mechanics , biology , model parameter , database
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.