Active noise cancellation of variable frequency narrow band noise using mixture of RLS and LMS algorithms

Yazdi, Hadi Sadoghi; Mehrabad, Ahmad Mardani; Mirghasemi, Saeed; Lotfizad, Mojtaba

doi:10.1504/ijsise.2009.033757

Cited by 4 publications

(7 citation statements)

References 39 publications

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“…The Literature survey investigates to facilitate different kinds of calculations else error inference techniques were exploited via adaptive filter to alter loads pro filter, furthermore error inference as indicated by EMG moreover noise features [4,5]. Best inclination supported calculations be least mean square (LMS), recursive least mean (RLS) along with various variations based on them [14]. Discussed methods experience a few issues of convergence, analyzing the non-linear and non-stationary processes, and partial overlap of signal and noise bandwidths.…”

Section: Research Review and Gapsmentioning

confidence: 99%

“…The explaining chronological research including research design, the adaptive filter research procedure works as a noise canceller due to its selflearning process in which filter coefficients are adjusted in iterations to minimize error in [29,30]. The ANC filter configuration be accounted for utilizing inclination based strategies, for example, LMS, also RLS techniques [14] utilized toward filter including power line also electroculogram (EOG) noise since ECG. As of late, developmental procedures, for example, QPSO, PSO, CS, MCS furthermore ABC, ABC-MR be utilized toward upgrade ANC channel aspects for effectively recreating ECG in [30].…”

Section: Gwo Based Anc Filter Devlopementioning

confidence: 99%

“…where e ij (m) is jth error of ith sample for mth iteration moreover M the total number of samples of input signal applied. After each iteration [13,14] a traditional adaptive filter optimization algorithm offers only one solution.…”

Section: Gwo Based Anc Filter Devlopementioning

confidence: 99%

See 2 more Smart Citations

A novel approach adaptive filtering method for electromyogram signal using Gray Wolf optimization algorithm

Verma¹,

Gupta²

2019

SN Appl. Sci.

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The proposed paper, presents the construction of adaptive noise cancellation filter based on gray wolf optimization (GWO) optimization technique.The relative investigation of different strategies uncovers that the presentation of GWO calculation is better in boisterous condition. The objective of proposed paper is structure ANC channel utilizing GWO method that improves association involving output with pure EMG signal.The results of proposed strategy are contrasted through gray wolf optimizer (GWO) and other evolutionary algorithms.The presentation of these calculations is assessed regarding signal-to-noise ratio (S SNR), mean square error (S MSE), maximum error (S ME) mean, convergence rate (CR) plus correlation feature (S r). The noise attenuation capability is tested on EMG signal contaminated with power line and ECG noise at different SNR levels. The ANC filter based on GWO technique provides 28 dB improvement in output SNR, 81% reduction in MSE, and 84% lower ME as compared to reported ANC filter based on RLS algorithm. Further, ANC filter based on GWO technique provides 7 dB improvement in output SNR, 59.5% reduction in MSE, and 69.2% lower ME as compared to recently reported ANC filter based on ABC-MR algorithm.

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Section: Research Review and Gapsmentioning

confidence: 99%

Section: Gwo Based Anc Filter Devlopementioning

confidence: 99%

See 1 more Smart Citation

A novel approach adaptive filtering method for electromyogram signal using Gray Wolf optimization algorithm

Verma¹,

Gupta²

2019

SN Appl. Sci.

View full text Add to dashboard Cite

show abstract

“…Explaining research chronological including research design, the research procedure in the form of an adaptive filter works as a noise canceller due to its self-learning process in which filter coefficients are adjusted in iterations in order to minimize the error [21]. The ANC filter design was reported using gradient based techniques such as LMS, and RLS methods [14] which were employed to filter the power line and electrooculogram (EOG) noise from ECG signal. Recently, the evolutionary techniques such as QPSO, PSO, CS, MCS and ABC, ABC-MR were used to optimize the ANC filter coefficients for efficiently reconstruction of ECG signal [22].…”

Section: Anc Filter Design Based On Br-abc Algorithmsmentioning

confidence: 99%

“…The literature review explores that various types of algorithms or error estimation methods have been exploited in adaptive filters to adjust the weights of filter, and error estimation according to EMG signal and noise properties [4,5]. Most efficient gradient based algorithms are least mean square (LMS), recursive least mean (RLS) and their different variants [14]. These techniques encounter some problems of convergence, analyzing the non-linear and non-stationary processes, and partial overlap of signal and noise band-widths.…”

Section: Introductionmentioning

confidence: 99%

Adaptive filtering method for EMG signal using bounded range artificial bee colony algorithm

2017

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In this paper, an adaptive artefact canceller is designed using the bounded range artificial bee colony (BR-ABC) optimization technique. The results of proposed method are compared with recursive least square and other evolutionary algorithms. The performance of these algorithms is evaluated in terms of signal-to-noise ratio (SNR), mean square error (MSE), maximum error (ME) mean, standard deviation (SD) and correlation factor (r). The noise attenuation capability is tested on EMG signal contaminated with power line and ECG noise at different SNR levels. A comparative study of various techniques reveals that the performance of BR-ABC algorithm is better in noisy environment. Our simulation results show that the ANC filter using BR-ABC technique provides 15 dB improvement in output average SNR, 63 and 83% reduction in MSE and ME, respectively as compared to ANC filter based on PSO technique. Further, the ANC filter designed using BR-ABC technique enhances the correlation between output and pure EMG signal.

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Active vibration control using nonlinear auto-regressive neural network to identify secondary channel

Chun-sheng,

Xue-chun,

et al. 2023

Journal of Low Frequency Noise, Vibration and Active Control

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The power unit on board the ship generates periodic low-frequency vibration that affects the normal operation of the equipment on board, and the adaptive feedforward control algorithm can effectively suppress such harmful vibration noise. But the adaptive feedforward control algorithm needs to obtain the identification model of the secondary channels, and the frequency domain least squares method based on the linear Extended auto-regressive model (ARX) is difficult to obtain the identification model with nonlinear characteristics. The nonlinear auto-regressive model (NARX) adds nonlinear mapping layers to the topology of the ARX model to enhance the identification capability of the NARX model for complex systems. In this paper, a block diagram of the Fx-LMS feedforward control algorithm based on the NARX model is proposed, then the initial parameters of the NARX neural network are optimized using the Quantum Particle Swarm Optimization (QPSO) algorithm and the secondary channel is identified, and the identification results show that the accuracy of identifying the secondary channel using the NARX neural network is higher than that of the ARX model. The simulation and experimental results show that the vibration damping effect of the proposed method is better than the traditional Fx-LMS method for both single-line spectrum and multi-line spectrum periodic low-frequency disturbances, which provides a new method for the suppression of periodic low-frequency disturbances.

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Active noise cancellation of variable frequency narrow band noise using mixture of RLS and LMS algorithms

Cited by 4 publications

References 39 publications

A novel approach adaptive filtering method for electromyogram signal using Gray Wolf optimization algorithm

A novel approach adaptive filtering method for electromyogram signal using Gray Wolf optimization algorithm

Adaptive filtering method for EMG signal using bounded range artificial bee colony algorithm

Active vibration control using nonlinear auto-regressive neural network to identify secondary channel

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