Maximum likelihood interval-varying recursive least squares identification for output-error autoregressive systems with scarce measurements (Q6168998)

scientific article; zbMATH DE number 7710262

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English	Maximum likelihood interval-varying recursive least squares identification for output-error autoregressive systems with scarce measurements	scientific article; zbMATH DE number 7710262

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instance of

scholarly article

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title

Maximum likelihood interval-varying recursive least squares identification for output-error autoregressive systems with scarce measurements (English)

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zbMATH Open document ID

1518.93144

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DOI

10.1016/j.jfranklin.2023.05.006

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Journal of the Franklin Institute

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publication date

11 July 2023

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Mathematics Subject Classification ID

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MaRDI publication profile

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full work available at URL

https://doi.org/10.1016/j.jfranklin.2023.05.006

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OpenAlex ID

W4378840483

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cites work

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Output prediction under scarce data operation: Control applications

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A note on the estimation of real- and complex-valued parameters in frequency domain maximum likelihood identification

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The filtering‐based maximum likelihood iterative estimation algorithms for a special class of nonlinear systems with autoregressive moving average noise using the hierarchical identification principle

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Maximum likelihood extended gradient‐based estimation algorithms for the input nonlinear controlled autoregressive moving average system with variable‐gain nonlinearity

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Maximum likelihood iterative identification approaches for multivariable equation-error moving average systems

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Recursive maximum likelihood estimation with \(t\)-distribution noise model

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Maximum likelihood estimation of Gaussian models with missing data -- Eight equivalent formulations

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Hierarchical Least Squares Identification for Linear SISO Systems With Dual-Rate Sampled-Data

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Adaptive parameter estimation for a general dynamical system with unknown states

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Recursive parameter estimation methods and convergence analysis for a special class of nonlinear systems

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Performance analysis of estimation algorithms of nonstationary ARMA processes

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Multi‐innovation gradient parameter estimation for multivariable systems based on the maximum likelihood principle

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Separable multi-innovation Newton iterative modeling algorithm for multi-frequency signals based on the sliding measurement window

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Auxiliary model multiinnovation stochastic gradient parameter estimation methods for nonlinear sandwich systems

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Separable synthesis gradient estimation methods and convergence analysis for multivariable systems

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Hierarchical parameter and state estimation for bilinear systems

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Partially-coupled nonlinear parameter optimization algorithm for a class of multivariate hybrid models

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Highly computationally efficient state filter based on the delta operator

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State estimation for bilinear systems through minimizing the covariance matrix of the state estimation errors

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A true three-scroll chaotic attractor coined

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Trial-and-error or avoiding a guess? Initialization of the Kalman filter

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An efficient hierarchical identification method for general dual-rate sampled-data systems

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Performance analysis of stochastic gradient algorithms under weak conditions

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Novel data filtering based parameter identification for multiple-input multiple-output systems using the auxiliary model

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mardi Publication:6168998