A neighborhood-based multiple orthogonal least square method for sparse signal recovery

Song, Yaru; Wu, Fei-Yun; Peng, Ru Lin

doi:10.1016/j.sigpro.2023.109044

Cited by 7 publications

(1 citation statement)

References 23 publications

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“…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.…”

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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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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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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An Automatic Threshold OMP Algorithm Based on QR Decomposition for Magnetic Resonance Image Reconstruction

Ni,

Wu,

Yang

2024

Circuits Syst Signal Process

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Joint parameter and time-delay estimation for a class of Wiener models based on a new orthogonal least squares algorithm

Liu,

Zhu

et al. 2024

Nonlinear Dyn

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This paper focuses on the identification of multiple inputs Wiener systems with piecewise-linear nonlinearity. The main objective is to jointly estimate the parameters and time-delays by using limited samples when the system orders are unknown. In order to identify the over-parameterized sparse system under the framework of greedy method, a Householder transformation-based greedy orthogonal least squares algorithm is proposed. In this scheme, the active columns of the information matrix are selected one by one according to a new greedy strategy, then the Householder transformation is employed to maintain an upper-triangular structure for the subinformation matrix, the back-substitution method is adopted to avoid the matrix inversion. Thus the problem of ill-conditioning frequently appears in nonlinear models can be eliminated. The Bayesian information criterion is used to choose the optimal sparsity level. Numerical experiments show that this new scheme can achieve almost optimal generalization performance while requiring less observations than the traditional schemes.

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A neighborhood-based multiple orthogonal least square method for sparse signal recovery

Cited by 7 publications

References 23 publications

Decomposition and composition modeling algorithms for control systems with colored noises

Decomposition and composition modeling algorithms for control systems with colored noises

An Automatic Threshold OMP Algorithm Based on QR Decomposition for Magnetic Resonance Image Reconstruction

Joint parameter and time-delay estimation for a class of Wiener models based on a new orthogonal least squares algorithm

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