Mean-Square Deviation Analysis of Multiband-Structured Subband Adaptive Filter Algorithm

Jeong, Jae Jin; Kim, Seung Hun; Koo, Gyogwon; Kim, Sang Woo

doi:10.1109/tsp.2015.2498136

Cited by 43 publications

(23 citation statements)

References 26 publications

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“…As stated in Remark 1, the ZA term in (5) is to make use of the sparsity of the underlying system to obtain an improvement in performance. However, any (m, j)-th entry of the matrix    , whose magnitude is less than and equal to NM so that it is much less than 1 especially for a long SAF [36], [38]. Furthermore, at each subband, the decimated subband input () i u kN is close to the white signal for large N [32].…”

Section: Proposed Algorithmsmentioning

confidence: 99%

“…The filter banks for partitioning the input signal () un and the desired signal () dn are assumed to be identical and paraunitary [32]- [36]. This assumption is to avoid the computation of the filter banks in the performance analyses.…”

Section: Assumptionmentioning

confidence: 99%

“…It is well-known that performance analysis of adaptive algorithms is always a research topic with many previously reported works in the literature, due to providing theoretical basis for designing adaptive filters [28]- [33]. The stochastic behavior of the NSAF algorithm was widely studied including the transient and steady -state performance [34]- [36]. To the best of our knowledge, however, there is no available performance analysis on the sparsity-aware NSAF algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Sparsity-aware subband adaptive algorithms with adjustable penalties

Zhao

Lamare

et al. 2019

Digital Signal Processing

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Section: Proposed Algorithmsmentioning

confidence: 99%

Section: Assumptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sparsity-aware subband adaptive algorithms with adjustable penalties

Zhao

Lamare

et al. 2019

Digital Signal Processing

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“…If the number of subbands is large enough, x i (k) may be approximately assumed white [26,28]. Therefore, by displacing the matrices in (8) and using (4), we obtain…”

Section: ∂ Jðwðkþ1þþ ∂Wðkþ1þmentioning

confidence: 99%

A low computational complexity normalized subband adaptive filter algorithm employing signed regressor of input signal

Abadi

Shafiee

Zalaghi

2018

EURASIP J. Adv. Signal Process.

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In this paper, the signed regressor normalized subband adaptive filter (SR-NSAF) algorithm is proposed. This algorithm is optimized by L 1 -norm minimization criteria. The SR-NSAF has a fast convergence speed and a low steady-state error similar to the conventional NSAF. In addition, the proposed algorithm has lower computational complexity than NSAF due to the signed regressor of the input signal at each subband. The theoretical mean-square performance analysis of the proposed algorithm in the stationary and nonstationary environments is studied based on the energy conservation relation and the steady-state, the transient, and the stability bounds of the SR-NSAF are predicated by the closed form expressions. The good performance of SR-NSAF is demonstrated through several simulation results in system identification, acoustic echo cancelation (AEC) and line EC (LEC) applications. The theoretical relations are also verified by presenting various experimental results.

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“…In SAAs, the mean square deviation method [22,23] and weighted average method [24,25] are used. Therefore, the SAAs can be illustrated as the pseudo-code below.…”

Section: Saas For Parameter Selectionmentioning

confidence: 99%

A Semi-blind Model with Parameter Identification for Building Temperature Estimation

et al. 2017

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An accurate thermal model for building enables the heating system (HS) to work efficiently as well as save energy. Thermal modelling often requires physical parameters of the building, which are difficult to be accurately determined. The aim of this work is to develop an optimal thermal model for better understanding of thermal dynamics with the goal of using this to estimate temperature variation in a few hours ahead within building. Based on the characteristics of thermal motion, a conventional physics-based (PB) model for building temperature estimation is introduced first. Afterwards, in order to refine the model and improve the actual performance, we propose an innovative semi-blind (SB) model based on data-driven approaches. Additionally, the methodologies including self-adaptive algorithms (SAAs) and grey prediction technique (GPT) have been applied in dealing with the integrated parameters estimation (IPE) process to ensure the practicability of the implemented model. The proposed model schema is validated by testing in a laboratory. The results indicate that the proposed approach achieves much higher accuracy in estimating temperature variation than the conventional PB model, with only limited knowledge of the building characteristics. The root mean square deviation (RMSD) of SB model and PB model are 0.18 and 0.43, respectively. According to the results, it can be conclu-

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Mean-Square Deviation Analysis of Multiband-Structured Subband Adaptive Filter Algorithm

Cited by 43 publications

References 26 publications

Sparsity-aware subband adaptive algorithms with adjustable penalties

Sparsity-aware subband adaptive algorithms with adjustable penalties

A low computational complexity normalized subband adaptive filter algorithm employing signed regressor of input signal

A Semi-blind Model with Parameter Identification for Building Temperature Estimation

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