The filtering based maximum likelihood recursive least squares estimation for multiple-input single-output systems

DOI10.1016/j.apm.2015.08.015zbMath1452.62693OpenAlexW2202084394MaRDI QIDQ2289158

Feiyan Chen, Feng Ding

Publication date: 28 January 2020

Published in: Applied Mathematical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.apm.2015.08.015

zbMATH Keywords

system identificationmaximum likelihoodleast squaresdata filteringmultiple-input systems

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Identification in stochastic control theory (93E12)

Related Items (14)

Data filtering based maximum likelihood extended gradient method for multivariable systems with autoregressive moving average noise ⋮ Adaptive gradient-based iterative algorithm for multivariable controlled autoregressive moving average systems using the data filtering technique ⋮ The relaxed gradient-based iterative algorithms for a class of generalized coupled Sylvester-conjugate matrix equations ⋮ New proof of the gradient-based iterative algorithm for the Sylvester conjugate matrix equation ⋮ A new adaptive identification framework for nonlinear multi-input multi-output systems under colored noise ⋮ Gradient-based iterative algorithms for generalized coupled Sylvester-conjugate matrix equations ⋮ Data filtering based multi-innovation extended gradient method for controlled autoregressive autoregressive moving average systems using the maximum likelihood principle ⋮ A novel weight function-based robust iterative learning identification method for discrete Box-Jenkins models with Student's \(t\)-distribution noises ⋮ The maximum likelihood least squares based iterative estimation algorithm for bilinear systems with autoregressive moving average noise ⋮ Optimal experiment design for identification of ARX models with constrained output in non-Gaussian noise ⋮ Computational fluid dynamics based dynamic modeling of parafoil system ⋮ Model recovery for Hammerstein systems using the auxiliary model based orthogonal matching pursuit method ⋮ Maximum likelihood gradient identification for multivariate equation‐error moving average systems using the multi‐innovation theory ⋮ Maximum likelihood based identification methods for rational models

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