2017 International Electrical Engineering Congress (iEECON) 2017

DOI: 10.1109/ieecon.2017.8075870

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A modified adaptive step-size affine projection algorithm based on QR-decomposition

Suchada Sitjongsataporn

¹

,

Piyaporn Nurarak

²

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Modified Kalman Vector Based On Qr-decomposition1

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2023

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Cited by 4 publications

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“…where 𝜃 denotes a unitary rotation. Consequently, a modified Kalman vector 𝜅 𝑘,𝑛 is derived from the postarray in the right-side of ( 7) as (8).…”

Section: Modified Kalman Vector Based On Qr-decompositionmentioning

confidence: 99%

Diffusion recursive least squares algorithm based on triangular decomposition

¹

,

Sitjongsataporn

²

,

³

2023

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<span lang="EN-US">In this paper, diffusion strategies used by QR-decomposition based on recursive least squares algorithm (DQR-RLS) and the sign version of DQR-RLS algorithm (DQR-sRLS) are introduced for distributed networks. In terms of the QR-decomposition method and Cholesky factorization, a modified Kalman vector is given adaptively with the help of unitary rotation that can decrease the complexity from inverse autocorrelation matrix to vector. According to the diffusion strategies, combine-then-adapt (CTA) and adapt-then-combine (ATC) based on DQR-RLS and DQR-sRLS algorithms are proposed with the combination and adaptation steps. To minimize the cost function, diffused versions of CTA-DQR-RLS, ATC-DQR-RLS, CTA-DQR-sRLS and ATC-DiQR-sRLS algorithms are compared. Simulation results depict that the proposed DQR-RLS-based and DQR-sRLS-based algorithms can clearly achieve the better performance than the standard combine-then-adapt-diffusion RLS (CTA-DRLS) and ATC-DRLS mechanisms.</span>

“…where 𝜃 denotes a unitary rotation. Consequently, a modified Kalman vector 𝜅 𝑘,𝑛 is derived from the postarray in the right-side of ( 7) as (8).…”

Section: Modified Kalman Vector Based On Qr-decompositionmentioning

confidence: 99%

Diffusion recursive least squares algorithm based on triangular decomposition

¹

,

Sitjongsataporn

²

,

³

2023

View full text Add to dashboard Cite

<span lang="EN-US">In this paper, diffusion strategies used by QR-decomposition based on recursive least squares algorithm (DQR-RLS) and the sign version of DQR-RLS algorithm (DQR-sRLS) are introduced for distributed networks. In terms of the QR-decomposition method and Cholesky factorization, a modified Kalman vector is given adaptively with the help of unitary rotation that can decrease the complexity from inverse autocorrelation matrix to vector. According to the diffusion strategies, combine-then-adapt (CTA) and adapt-then-combine (ATC) based on DQR-RLS and DQR-sRLS algorithms are proposed with the combination and adaptation steps. To minimize the cost function, diffused versions of CTA-DQR-RLS, ATC-DQR-RLS, CTA-DQR-sRLS and ATC-DiQR-sRLS algorithms are compared. Simulation results depict that the proposed DQR-RLS-based and DQR-sRLS-based algorithms can clearly achieve the better performance than the standard combine-then-adapt-diffusion RLS (CTA-DRLS) and ATC-DRLS mechanisms.</span>

Diffusion inverse Square-root Recursive Least Square-based Algorithm with Adaptive Network

Sitjongsataporn

¹

,

²

,

³

2021

2021 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technolo

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No abstract

An Adaptive inverse Square-root Affine Projection Sign Algorithm based on QR-Decomposition

Sitjongsataporn

¹

,

²

,

³

2020

2020 International Conference on Power, Energy and Innovations (ICPEI)

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No abstract

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