Generalized adaptive weighted recursive least squares dictionary learning

Naderahmadian, Yashar; Tinati, Mohammad Ali; Beheshti, Soosan

doi:10.1016/j.sigpro.2015.06.013

Cited by 23 publications

(11 citation statements)

References 29 publications

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“…It is shown that RLS-DLA is a special case of GAW-RLS when 1  = (  is the weight that is considered for the new training data). Therefore, the cost function in (7) changes to [26]:…”

Section: Rls-dla and Gaw-rlsmentioning

confidence: 99%

“…The rest of the paper is organized as follows: In Section 2, the dictionary learning problem is given. In Section 3, a brief review of RLS-DLA and generalized adaptive weighted recursive least squares dictionary learning (GAW-RLS) [26] algorithms are provided. The proposed CBW-RLS dictionary learning algorithm, is presented in Section 4.…”

Section: Introductionmentioning

confidence: 99%

“…by substituting(19) and(22) in(21), and defining the following variables: parameter that controls the step size of the dictionary updating. Therefore, (22) can be written as: the next iteration, (12)-(26) are repeated until i reaches L. Algorithm 1 shows the summary of CBW-RLS dictionary learning algorithm.…”

mentioning

confidence: 99%

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Recursive Dictionary Learning Approach Exploiting Between-Channel Correlations for EEG Signal Reconstruction

Vazifehkhahi¹,

Rezaii²

2020

Preprint

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In tele-monitoring, wireless body area networks (WBANs), sleep analysis and other applications involving electroencephalogram (EEG) signal, due to the high number of EEG recording channels, long recording time and several repetition of recordings to reach the highest signal-to-noise ratio, the amount of acquired data by the sensors is too large, demanding use of some compression procedure. Compressed sensing can be considered as one of the most effective compression methods in terms of compression ratio, which needs the underlying signal be sparse or have sparse representation in an appropriate domain. EEG signal is not sparse in time domain, therefore, in this paper correlation based weighted recursive least squares dictionary learning algorithm (CBW-RLS) is proposed that uses between-channel correlations to sparsify EEG signals. Due to the low-rank structure of EEG signals, exploiting between-channel correlations increase the sparsity level of the model while reducing the computational cost of dictionary learning procedure. This is done by merely updating the dictionary atoms which are involved in the sparse model of the previous data, reducing the total number of data used at each iteration and speeding up the dictionary learning algorithm. The simulation results show that the proposed method has better performance in terms of both quality of the EEG reconstruction and the computational cost compared to the other methods.

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“…It is shown that RLS-DLA is a special case of GAW-RLS when 1  = (  is the weight that is considered for the new training data). Therefore, the cost function in (7) changes to [26]:…”

Section: Rls-dla and Gaw-rlsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Recursive Dictionary Learning Approach Exploiting Between-Channel Correlations for EEG Signal Reconstruction

Vazifehkhahi¹,

Rezaii²

2020

Preprint

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show abstract

“…In this regard, a large forgetting factor should be assigned to the parameters changing slowly to guarantee the stability of the algorithm, while a small forgetting factor is more appropriate for the tracking of fast varying parameter. In seeking to address this problem, the VFFRELS with multiple forgetting factors [41][42][43] for identification is applied in this paper. With the VFFRELS, the forgetting factors can be decoupled and tuned separately to improve the parameters stability and tracking accuracy of SOC estimation.…”

Section: Parameters Identificationmentioning

confidence: 99%

Online State of Charge Estimation for Lithium-Ion Battery by Combining Incremental Autoregressive and Moving Average Modeling with Adaptive H-Infinity Filter

Liu

Dang

2018

Mathematical Problems in Engineering

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The state of charge (SOC) estimation is one of the most important features in battery management system (BMS) for electric vehicles (EVs). In this article, a novel equivalent-circuit model (ECM) with an extra noise sequence is proposed to reduce the adverse effect of model error. Model parameters identification method with variable forgetting factor recursive extended least squares (VFFRELS), which combines a constructed incremental autoregressive and moving average (IARMA) model with differential measurement variables, is presented to obtain the ECM parameters. The independent open circuit voltage (OCV) estimator with error compensation factors is designed to reduce the OCV error of OCV fitting model. Based on the IARMA battery model analysis and the parameters identification, an SOC estimator by adaptive H-infinity filter (AHIF) is formulated. The adaptive strategy of the AHIF improves the numerical stability and robust performance by synchronous adjusting noise covariance and restricted factor. The results of experiment and simulation have verified that the proposed approach has superior advantage of parameters identification and SOC estimation to other estimation methods.

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“…However, the RLS with constant forgetting factor may encounter the difficulties of balancing between stability and convergence. Seeking to address this problem, we apply the VFFRLS with variable forgetting factors [44][45][46] for identification in this paper. The process of parameters estimation of VFFRLS is realized as follows:…”

Section: Parameters Identificationmentioning

confidence: 99%

A New Method for State of Charge and Capacity Estimation of Lithium-Ion Battery Based on Dual Strong Tracking Adaptive H Infinity Filter

Liu

Dang

2018

Mathematical Problems in Engineering

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As one of the most important features representing the operating state of power battery in electric vehicles (EVs), state of charge (SOC) and capacity estimation is a crucial assessment index in battery management system (BMS). This paper presents a fusion method of SOC and capacity estimation with identified model parameters. The equivalent circuit model (ECM) parameters are obtained online by variable forgetting factor recursive least squares (VFFRLS), which is based on incremental ECM analysis to respond to the inconsistent rates of parameters variation. The independent open-circuit voltage (OCV) estimation way is designed to reduce the effect of mutual coupling between OCV and ECM parameters. Based on the identified ECM parameters and OCV, a dual adaptive H infinity filter (AHIF) combined with strong tracking filter (STF) is proposed to estimate battery SOC and capacity. A new quadratic function as capacity error compensation is introduced to represent the relationship between capacity and OCV. The adaptive strategy of the AHIF can adjust noise covariance and restricted factor, while the STF can regulate prior state covariance by adding suboptimum fading factor. The results of experiment and simulation show the merits of proposed approach in SOC and capacity estimation.

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Generalized adaptive weighted recursive least squares dictionary learning

Cited by 23 publications

References 29 publications

Recursive Dictionary Learning Approach Exploiting Between-Channel Correlations for EEG Signal Reconstruction

Recursive Dictionary Learning Approach Exploiting Between-Channel Correlations for EEG Signal Reconstruction

Online State of Charge Estimation for Lithium-Ion Battery by Combining Incremental Autoregressive and Moving Average Modeling with Adaptive H-Infinity Filter

A New Method for State of Charge and Capacity Estimation of Lithium-Ion Battery Based on Dual Strong Tracking Adaptive H Infinity Filter

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