Performance analysis of the recursive parameter estimation algorithms for multivariable Box-Jenkins systems
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Publication:1660344
DOI10.1016/j.jfranklin.2014.07.004zbMath1395.93521OpenAlexW2082504143MaRDI QIDQ1660344
Publication date: 16 August 2018
Published in: Journal of the Franklin Institute (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jfranklin.2014.07.004
System identification (93B30) Multivariable systems, multidimensional control systems (93C35) Estimation and detection in stochastic control theory (93E10) Least squares and related methods for stochastic control systems (93E24) Identification in stochastic control theory (93E12)
Related Items (12)
Frequency-shifting-based stable on-line algebraic parameter identification of linear systems ⋮ Joint estimation of states and parameters for an input nonlinear state-space system with colored noise using the filtering technique ⋮ Recursive parameter estimation algorithms and convergence for a class of nonlinear systems with colored noise ⋮ Convergence of the auxiliary model-based multi-innovation generalized extended stochastic gradient algorithm for Box-Jenkins systems ⋮ Gradient-based iterative identification methods for multivariate pseudo-linear moving average systems using the data filtering ⋮ Parameter identification of a class of nonlinear systems based on the multi-innovation identification theory ⋮ Recursive least squares identification of hybrid Box-Jenkins model structure in open-loop and closed-loop ⋮ Robust identification of OE model with constrained output using optimal input design ⋮ Wiener structure based adaptive control for dynamic processes with approximate monotonic nonlinearities ⋮ Filtering based parameter estimation for observer canonical state space systems with colored noise ⋮ A novel weight function-based robust iterative learning identification method for discrete Box-Jenkins models with Student's \(t\)-distribution noises ⋮ Two-stage multi-innovation stochastic gradient algorithm for multivariate output-error ARMA systems based on the auxiliary model
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