Identification of partially known non-linear stochastic spatio-temporal dynamical systems by using a novel partially linear Kernel method - Ning, Hanwen; Jing, Xingjian (IET control theory & applications, 2015)
Keywords: control system synthesis; Design analysis; Design engineering; Dynamical systems; extended partially linear least square ridge regression model; Hilbert spaces; identification method; Identification methods; kernel Hilbert space‐based algorithm; least mean squares methods; MIMO systems; model parameter estimation; multiple input multiple output extended partially linear model; nonlinear dynamical systems; Nonlinear dynamics; nonlinear estimation; nonlinear stochastic partial differential dynamical system; nonlinear stochastic spatiotemporal dynamical system identification; nonlinear system analysis; nonlinear system design; Nonlinearity; Partial differential equations; partially linear kernel method; partially linear structural information; physical model; regression analysis; spatiotemporal phenomena; state‐space methods; state‐space model; stochastic partial differential equations; stochastic systems; Stochasticity; least mean squares methods; regression analysis; nonlinear estimation; nonlinear dynamical systems; identification method; kernel Hilbert space-based algorithm; model parameter estimation; stochastic systems; partially linear structural information; nonlinear system design; nonlinear stochastic spatiotemporal dynamical system identification; stochastic partial differential equations; control system synthesis; Hilbert spaces; state-space model; nonlinear stochastic partial differential dynamical system; spatiotemporal phenomena; partially linear kernel method; physical model; MIMO systems; nonlinear system analysis; partial differential equations; multiple input multiple output extended partially linear model; state-space methods; extended partially linear least square ridge regression model