NDLI: Identification of partially known non-linear stochastic spatio-temporal dynamical systems by using a novel partially linear Kernel method

Content Provider	IET Digital Library
Author	Ning, Hanwen Jing, Xingjian
Abstract	The identification of non-linear stochastic spatio-temporal dynamical systems given by stochastic partial differential equations is of great significance to engineering practice, since it can always provide useful insight into the mechanism and physical characteristics of the underlying dynamics. In this study, based on the difference method for stochastic partial differential equations, a novel state-space model named multi-input–multi-output extended partially linear model for stochastic spatio-temporal dynamical system is proposed. A new Reproducing Kernel Hilbert Space-based algorithm named extended partially linear least square ridge regression is thus particularly developed for the identification of the extended partially linear model. Compared with existing identification methods available for spatio-temporal dynamics, the advantages of the proposed identification method include that (i) it can make full use of the partially linear structural information of physical models, (ii) it can achieve more accurate estimation results for system non-linear dynamics and (iii) the resulting estimated model parameters have clear physical meaning or properties closely related to the underlying dynamical system. Moreover, the proposed extended partially linear model also provide a convenient state-space model for system analysis and design (e.g. controller or filter design) of the class of non-linear stochastic partial differential dynamical systems.
Starting Page	21
Ending Page	33
Page Count	13
ISSN	17518644
Volume Number	9
e-ISSN	17518652
Issue Number	Issue 1, Jan (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cta/9/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cta.2014.0242
Journal	IET Control Theory & Applications
Publisher Date	2014-09-24
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Control System Analysis Control System Synthesis Differential Equation (numerical Analysis) Extended Partially Linear Least Square Ridge Regression Model Hilbert Space Identification Method Interpolation And Function Approximation Kernel Hilbert Space-based Algorithm Least Mean Squares Method MIMO System Model Parameter Estimation Modelling And Identification Multiple Input Multiple Output Extended Partially Linear Model Multivariable Control System Nonlinear Control System Nonlinear Dynamical System Nonlinear Estimation Nonlinear Stochastic Partial Differential Dynamical System Nonlinear Stochastic Spatiotemporal Dynamical System Identification Nonlinear System Analysis Nonlinear System Design Numerical Analysis Partial Differential Equation Partially Linear Kernel Method Partially Linear Structural Information Physical Model Regression Analysis Simulation Spatiotemporal Phenomena State Space Method State-Space Model Statistics Stochastic Partial Differential Equation Stochastic System Synthesis Method Time Varying Control System
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Human-Computer Interaction Electrical and Electronic Engineering Computer Science Applications

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