A new Mean Preserving Moving Least Squares method for Arbitrary Order Finite Volume schemes

Ramírez, Luis; Edreira, Laura; Couceiro, Iván; Ouro, Pablo; Nogueira, Xesús; Colominas, Ignasi

doi:10.1016/j.amc.2022.127768

Cited by 3 publications

(2 citation statements)

References 48 publications

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“…Step 5: Compute the gain vector L 2 (t) through (26) and compute the covariance matrix P 2 (t) via (27).…”

Section: Decomposition Based Recursive Generalized Extended Least Squ...mentioning

confidence: 99%

“…The LS approach is considerable promising and appealing in system identification and used widely to optimize the objective function regarding as the model parameters. Recently, many improved LS methods have been proposed such as the subspaces coordinate least‐squares method, 24 the neighborhood‐based multiple orthogonal least squares method 25 and the mean preserving moving least squares method 26 and so forth. For solving system identification problems, Pan et al combined the least squares optimization into the multi‐innovation gradient scheme to present a combinational least squares algorithm and used it to identify the multivariable CAR‐like system 27 .…”

Section: Introductionmentioning

confidence: 99%

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Decomposition and composition modeling algorithms for control systems with colored noises

Xu,

Ding

2023

Adaptive Control & Signal

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SummaryThis article proposes a novel identification framework for estimating the parameters of the controlled autoregressive autoregressive moving average (CARARMA) models with colored noise. By means of building an auxiliary model and using the hierarchical identification principle, this article investigates a highly‐efficient parameter estimation algorithm. In order to meet the need for identifying the systems with large‐scale parameters, the whole parameters of the CARARMA system is separated into two parameter sets and a decomposition and composition recursive identification algorithm (i.e., hierarchical generalized extended least squares algorithm or decomposition‐based recursive generalized extended least squares algorithm) is presented. Moreover, this article analyzes the convergence of the proposed decomposition and composition recursive identification algorithm. The performance analysis shows that the proposed decomposition and composition identification algorithm can reduce the complexity of the identification algorithm compared with the algorithm without decomposition.

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“…Step 5: Compute the gain vector L 2 (t) through (26) and compute the covariance matrix P 2 (t) via (27).…”

Section: Decomposition Based Recursive Generalized Extended Least Squ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decomposition and composition modeling algorithms for control systems with colored noises

Xu,

Ding

2023

Adaptive Control & Signal

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show abstract

Preface for the special issue “Hyperbolic PDE in computational physics: Advanced mathematical models and structure-preserving numerics”

Dumbser

Busto

Vázquez-Cendón

et al. 2023

Applied Mathematics and Computation

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Compressible FSI of elastic spikes for drag reduction under hypersonic flow

Wang,

Mei,

Wang

et al. 2024

International Journal of Mechanical Sciences

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A new Mean Preserving Moving Least Squares method for Arbitrary Order Finite Volume schemes

Cited by 3 publications

References 48 publications

Decomposition and composition modeling algorithms for control systems with colored noises

Decomposition and composition modeling algorithms for control systems with colored noises

Preface for the special issue “Hyperbolic PDE in computational physics: Advanced mathematical models and structure-preserving numerics”

Compressible FSI of elastic spikes for drag reduction under hypersonic flow

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