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Journal articles on the topic 'Filtered-X algorithm'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Saeki, Takuma, Kojiro Matsushita, Satoshi Ito, and Minoru Sasaki. "Vibration Control of a Flexible Manipulator Using Filtered-x LMS Algorithm." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2015.6 (2015): 341–42. http://dx.doi.org/10.1299/jsmeicam.2015.6.341.

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2

DeBrunner, V. E., and Dayong Zhou. "Hybrid filtered error LMS algorithm: another alternative to filtered-x LMS." IEEE Transactions on Circuits and Systems I: Regular Papers 53, no. 3 (March 2006): 653–61. http://dx.doi.org/10.1109/tcsi.2005.859574.

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3

Bjarnason, E. "Analysis of the filtered-X LMS algorithm." IEEE Transactions on Speech and Audio Processing 3, no. 6 (1995): 504–14. http://dx.doi.org/10.1109/89.482218.

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4

Kwon, Oh Sang. "Analysis of Bi-directional Filtered-x Least Mean Square Algorithm." Journal of the Korea Society of Digital Industry and Information Management 10, no. 4 (December 30, 2014): 133–42. http://dx.doi.org/10.17662/ksdim.2014.10.1.133.

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5

Shyu, Kuo-Kai, Cheng-Yuan Chang, and Ming-Chu Kuo. "Self-tuning controller with fuzzy filtered-X algorithm." Electronics Letters 36, no. 2 (2000): 182. http://dx.doi.org/10.1049/el:20000189.

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6

Wang, Lei, Kean Chen, Jian Xu, and Wang Qi. "Simplified fast transversal filter algorithms for multichannel active noise control." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 2 (August 1, 2021): 4683–91. http://dx.doi.org/10.3397/in-2021-2793.

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In recent years, more attention has been paid to the performance of algorithm in active noise control (ANC). Compared with filtered-x LMS (FxLMS) algorithm based on stochastic gradient descent, filtered-x RLS (FXRLS) algorithm has faster convergence speed and better tracking performance at the cost of high computational complexity. In order to reduce the computation, fast transversal filter (FTF) algorithm can be used in ANC system. In this paper, simplified multi-channel FXFTF algorithms are presented, and the convergence speed and noise reduction performance of different multichannel algorithms are simulated and compared, and the numerical stability of the algorithms are analyzed.

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7

Sun, Wei, Yi Nong Li, Feng Zhang, and Gui Yan Li. "Active Gear Pair Vibration Control Based on Filtered-X RLS Algorithm." Applied Mechanics and Materials 86 (August 2011): 166–69. http://dx.doi.org/10.4028/www.scientific.net/amm.86.166.

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Based on the investigation of active gear pair vibration control system, an adaptive controller combined with Filtered-X method and RLS algorithm is developed to reduce the periodic vibration of gear driven shaft. The active control of the gear shaft transverse vibration is simulated to validate the efficiency of the proposed Filtered-X RLS algorithm (FXRLS). The results indicate that the FXRLS is significantly better in convergence speed and stability than the commonly used Filtered-X LMS algorithm (FXLMS), and the stability and convergence are more robust.

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8

Avalos, J. G., A. Rodriguez, H. M. Martinez, J. C. Sanchez, and H. M. Perez. "Multichannel Filtered-X Error Coded Affine Projection-Like Algorithm with Evolving Order." Shock and Vibration 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/3864951.

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Affine projection (AP) algorithms are commonly used to implement active noise control (ANC) systems because they provide fast convergence. However, their high computational complexity can restrict their use in certain practical applications. The Error Coded Affine Projection-Like (ECAP-L) algorithm has been proposed to reduce the computational burden while maintaining the speed of AP, but no version of this algorithm has been derived for active noise control, for which the adaptive structures are very different from those of other configurations. In this paper, we introduce a version of the ECAP-L for single-channel and multichannel ANC systems. The proposed algorithm is implemented using the conventional filtered-x scheme, which incurs a lower computational cost than the modified filtered-x structure, especially for multichannel systems. Furthermore, we present an evolutionary method that dynamically decreases the projection order in order to reduce the dimensions of the matrix used in the algorithm’s computations. Experimental results demonstrate that the proposed algorithm yields a convergence speed and a final residual error similar to those of AP algorithms. Moreover, it achieves meaningful computational savings, leading to simpler hardware implementation of real-time ANC applications.

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9

Tobias, O. J., J. C. M. Bermudez, and N. J. Bershad. "Mean weight behavior of the filtered-X LMS algorithm." IEEE Transactions on Signal Processing 48, no. 4 (April 2000): 1061–75. http://dx.doi.org/10.1109/78.827540.

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10

Miyoshi, S., and Y. Kajikawa. "Statistical-mechanics approach to the filtered-X LMS algorithm." Electronics Letters 47, no. 17 (2011): 997. http://dx.doi.org/10.1049/el.2011.1691.

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11

Avalos Ochoa, Juan Gerardo, Giovanny Sanchez Rivera, Alejandro Rodriguez Silva, Jonathan Mendoza Guevara, and Guillermo Avalos Arzate. "Multichannel Filtered-X Set-Membership Affine Projection-Like Algorithm." IEEE Latin America Transactions 16, no. 8 (August 2018): 2131–37. http://dx.doi.org/10.1109/tla.2018.8528226.

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12

Lovstedt, Stephan P., Jared K. Thomas, Scott D. Sommerfeldt, and Jonathan D. Blotter. "Genetic Algorithm Applied to the Eigenvalue Equalization Filtered-x LMS Algorithm (EE-FXLMS)." Advances in Acoustics and Vibration 2008 (May 8, 2008): 1–12. http://dx.doi.org/10.1155/2008/791050.

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The FXLMS algorithm, used extensively in active noise control (ANC), exhibits frequency-dependent convergence behavior. This leads to degraded performance for time-varying tonal noise and noise with multiple stationary tones. Previous work by the authors proposed the eigenvalue equalization filtered-x least mean squares (EE-FXLMS) algorithm. For that algorithm, magnitude coefficients of the secondary path transfer function are modified to decrease variation in the eigenvalues of the filtered-x autocorrelation matrix, while preserving the phase, giving faster convergence and increasing overall attenuation. This paper revisits the EE-FXLMS algorithm, using a genetic algorithm to find magnitude coefficients that give the least variation in eigenvalues. This method overcomes some of the problems with implementing the EE-FXLMS algorithm arising from finite resolution of sampled systems. Experimental control results using the original secondary path model, and a modified secondary path model for both the previous implementation of EE-FXLMS and the genetic algorithm implementation are compared.

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13

Kuo Wang, A., and Wei Ren. "Convergence Analysis of the Multi-Variable Filtered-X LMS Algorithm." IFAC Proceedings Volumes 29, no. 1 (June 1996): 5024–29. http://dx.doi.org/10.1016/s1474-6670(17)58477-3.

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14

Mondal (Das), Kuheli. "An Improved All-Pass Filtered x Least Mean Square Algorithm." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 1.5 (September 15, 2020): 178–84. http://dx.doi.org/10.30534/ijatcse/2020/2591.52020.

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15

Tanaka, Masashi, Yutaka Kaneda, and Junji Kojima. "A fast converging adaptive filter algorithm for filtered-x systems." Journal of the Acoustical Society of Japan (E) 17, no. 6 (1996): 311–21. http://dx.doi.org/10.1250/ast.17.311.

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16

Swanson, D. C., S. M. Hirsch, K. M. Reichard, and J. Tichy. "Development of a frequency-domain filtered-x intensity ANC algorithm." Applied Acoustics 57, no. 1 (May 1999): 39–49. http://dx.doi.org/10.1016/s0003-682x(98)00046-2.

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17

Chang, C. Y., and K. K. Shyu. "Active noise cancellation with a fuzzy adaptive filtered-X algorithm." IEE Proceedings - Circuits, Devices and Systems 150, no. 5 (2003): 416. http://dx.doi.org/10.1049/ip-cds:20030406.

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18

Sommerfeldt, Scott D., and Peter J. Nashif. "An adaptive filtered‐x algorithm for energy‐based active control." Journal of the Acoustical Society of America 96, no. 1 (July 1994): 300–306. http://dx.doi.org/10.1121/1.411308.

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19

Douglas, S. C. "An efficient implementation of the modified filtered-X LMS algorithm." IEEE Signal Processing Letters 4, no. 10 (October 1997): 286–88. http://dx.doi.org/10.1109/97.633770.

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20

Albu, F. "Efficient multichannel filtered-x affine projection algorithm for active noisecontrol." Electronics Letters 42, no. 7 (2006): 421. http://dx.doi.org/10.1049/el:20063966.

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21

Fraanje, R., M. Verhaegen, and N. Doelman. "Increasing the Robustness of a Preconditioned Filtered-X LMS Algorithm." IEEE Signal Processing Letters 11, no. 2 (February 2004): 285–88. http://dx.doi.org/10.1109/lsp.2003.819875.

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22

Mouli, A. Chandra, and Ch Ratnam. "A Novel Approach to Active Noise Control using Normalized Clipped Adaptive Algorithm." International Journal of Manufacturing, Materials, and Mechanical Engineering 3, no. 3 (July 2013): 76–88. http://dx.doi.org/10.4018/ijmmme.2013070105.

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In this paper, an efficient normalization based adaptive algorithm is used for active noise control in mechanical systems in order to reject extensive disturbances. The proposed implementations are suitable in applications like various motors, generators, aircrafts, battle field and elevators, etc where noise reduction is very important. In the experiments, the authors used several variants of the familiar Filtered X Least Mean Square (FXLMS) algorithm. In FXLMS the vector of past inputs is first filtered by the secondary path transfer function, hence it is named as filtered X LMS. These modified results normalized FXLMS (NFXLMS) and normalized clipped FXLMS (NCFXLMS) algorithms, leads to fast convergence, better noise rejection capability. The NCFXLMS algorithm requires only half of the multiplications requires than NFXLMS. This type of low complexity strategy is not used in active noise control application in mechatronic systems. Simulation results prove that the proposed active noise cancellers provide better performance in terms of signal to noise ratio than the conventional FXLMS.

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23

Li, Chao, and Zhangwei Chen. "A fast vibration-level adjustment method for low-frequency vibration calibration based on modified filtered-x least mean square algorithm." Measurement and Control 53, no. 3-4 (January 7, 2020): 328–38. http://dx.doi.org/10.1177/0020294019881727.

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Traditionally, successive approximation method is applied to the vibration-level adjustment of vibration calibration system, which leads to a time-consuming work for low-frequency vibration calibration. In this paper, a novel control method for low-frequency vibration calibration system is proposed based on adaptive filter. First, the problem of the traditional vibration-level adjustment for low-frequency signals is depicted. Then, an adaptive control algorithm is presented, in which the control input is composed of two weighted sinusoidal signals with a phase difference of 90°. The weighted vector is updated in real time using a modified filtered-x least mean square algorithm. Unlike filtered-x least mean square algorithm, the proposed modified filtered-x least mean square algorithm does not require a pre-identification of the controlled system and has a reduced computational complexity. The convergence property of the proposed method is analyzed in detail. Finally, the proposed method is implemented on a low-frequency vibration calibration system. Experimental results show that the proposed modified filtered-x least mean square algorithm can significantly reduce the time of the vibration-level adjustment in low-frequency band.

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24

Fang, Yubin, Xiaojin Zhu, Zhiyuan Gao, Jiaming Hu, and Jian Wu. "New feedforward filtered-x least mean square algorithm with variable step size for active vibration control." Journal of Low Frequency Noise, Vibration and Active Control 38, no. 1 (November 14, 2018): 187–98. http://dx.doi.org/10.1177/1461348418812326.

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The step size of least mean square (LMS) algorithm is significant for its performance. To be specific, small step size can get small excess mean square error but results in slow convergence. However, large step size may cause instability. Many variable step size least mean square (VSSLMS) algorithms have been developed to enhance the control performance. In this paper, a new VSSLMS was proposed based on Kwong’s algorithm to evaluate the robustness. The approximate analysis of dynamic and steady-state performance of this developed VSSLMS algorithm was given. An active vibration control system of piezoelectric cantilever beam was established to verify the performance of the VSSLMS algorithms. By comparing with the current VSSLMS algorithms, the proposed method has better performance in active vibration control applications.

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25

Lee. "A Filtered-x Affine Projection Sign Algorithm with Improved Convergence Rate for Active Impulsive Noise Control." Journal of the Acoustical Society of Korea 34, no. 2 (2015): 130. http://dx.doi.org/10.7776/ask.2015.34.2.130.

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26

Sahib, Mouayad A., and Raja Kamil. "Comparison of Performance and Computational Complexity of Nonlinear Active Noise Control Algorithms." ISRN Mechanical Engineering 2011 (September 6, 2011): 1–9. http://dx.doi.org/10.5402/2011/925085.

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Research on nonlinear active noise control (NANC) revolves around the investigation of the sources of nonlinearity as well as the performance and computational load of the nonlinear algorithms. The nonlinear sources could originate from the noise process, primary and secondary propagation paths, and actuators consisting of loudspeaker, microphone or amplifier. Several NANCs including Volterra filtered-x least mean square (VFXLMS), bilinear filtered-x least mean square (BFXLMS), and filtered-s least mean square (FSLMS) have been utilized to overcome these nonlinearities effects. However, the relative performance and computational complexities of these algorithm in comparison to FXLMS algorithm have not been carefully studied. In this paper, systematic comparisons of the FXLMS against the nonlinear algorithms are evaluated in overcoming various nonlinearity sources. The evaluation of the algorithms performance is standardized in terms of the normalized mean square error while the computational complexity is calculated based on the number of multiplications and additions in a single iteration. Computer simulations show that the performance of the FXLMS is more than 80% of the most effective nonlinear algorithm for each type of nonlinearity sources at the fraction of computational load. The simulation results also suggest that it is more advantageous to use FXLMS for practical implementation of NANC.

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27

Chang, Cheng-Yuan, and Fang-Bor Luoh. "Enhancement of active noise control using neural-based filtered-X algorithm." Journal of Sound and Vibration 305, no. 1-2 (August 2007): 348–56. http://dx.doi.org/10.1016/j.jsv.2007.04.007.

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28

Song, Pucha, and Haiquan Zhao. "Filtered-x generalized mixed norm (FXGMN) algorithm for active noise control." Mechanical Systems and Signal Processing 107 (July 2018): 93–104. http://dx.doi.org/10.1016/j.ymssp.2018.01.035.

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29

Tang, X. L., and C. M. Lee. "Time–frequency-domain filtered-x LMS algorithm for active noise control." Journal of Sound and Vibration 331, no. 23 (November 2012): 5002–11. http://dx.doi.org/10.1016/j.jsv.2012.07.009.

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30

Carini, Alberto, and Giovanni L. Sicuranza. "Optimal Regularization Parameter of the Multichannel Filtered-$x$ Affine Projection Algorithm." IEEE Transactions on Signal Processing 55, no. 10 (October 2007): 4882–95. http://dx.doi.org/10.1109/tsp.2007.896113.

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31

Lu, Lu, and Haiquan Zhao. "Improved Filtered-x Least Mean Kurtosis Algorithm for Active Noise Control." Circuits, Systems, and Signal Processing 36, no. 4 (August 3, 2016): 1586–603. http://dx.doi.org/10.1007/s00034-016-0379-3.

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32

Guo, Jianfeng, Feiran Yang, and Jun Yang. "Mean-Square Performance of the Modified Filtered-x Affine Projection Algorithm." Circuits, Systems, and Signal Processing 39, no. 8 (February 11, 2020): 4243–57. http://dx.doi.org/10.1007/s00034-020-01365-2.

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33

Yang, Feiran, Yin Cao, Ming Wu, Felix Albu, and Jun Yang. "Frequency-Domain Filtered-x LMS Algorithms for Active Noise Control: A Review and New Insights." Applied Sciences 8, no. 11 (November 20, 2018): 2313. http://dx.doi.org/10.3390/app8112313.

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This paper presents a comprehensive overview of the frequency-domain filtered-x least mean-square (FxLMS) algorithms for active noise control (ANC). The direct use of frequency-domain adaptive filters for ANC results in two kinds of delays, i.e., delay in the signal path and delay in the weight adaptation. The effects of the two kinds of delays on the convergence behavior and stability of the adaptive algorithms are analyzed in this paper. The first delay can violate the so-called causality constraint, which is a major concern for broadband ANC, and the second delay can reduce the upper bound of the step size. The modified filter-x scheme has been employed to remove the delay in the weight adaptation, and several delayless filtering approaches have been presented to remove the delay in the signal path. However, state-of-the-art frequency-domain FxLMS algorithms only remove one kind of delay, and some of these algorithms have a very high peak complexity and hence are impractical for real-time systems. This paper thus proposes a new delayless frequency-domain ANC algorithm that completely removes the two kinds of delays and has a low complexity. The performance advantages and limitations of each algorithm are discussed based on an extensive evaluation, and the complexities are evaluated in terms of both the peak and average complexities.

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34

Yu, Ning, Zhaoxia Li, Yinfeng Wu, Renjian Feng, and Bin Chen. "Convex combination-based active impulse noise control system." Journal of Low Frequency Noise, Vibration and Active Control 39, no. 1 (March 25, 2019): 190–202. http://dx.doi.org/10.1177/1461348419838394.

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Active noise control shows a good performance on the suppression of the low-frequency noise and hence it is widely applied. However, the traditional active noise control systems are unsatisfactory in controlling impulse noise in practical situations. A method based on the convex combination of filtered-x least mean square and filtered-x minimum kernel risk-sensitive loss adaptive algorithms (CFxLM) is presented to efficiently suppress impulse noise. Due to the simplicity of the LMS algorithm, the related filter is selected as the fast filter. Because the minimum kernel risk-sensitive loss algorithm is robust to impulse noise and can offer good convergence performance, we first apply it to the active noise control system and select the corresponding filter as the slow one. The proposed CFxLM algorithm can achieve both fast convergence and good noise reduction and any prior knowledge of reference noise is unnecessary. Extensive simulations demonstrate the superior noise reduction capability of the developed CFxLM-based active noise control system in controlling impulse noise.

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35

Vázquez, Ángel A., Eduardo Pichardo, Juan G. Avalos, Giovanny Sánchez, Hugo M. Martínez, Juan C. Sánchez, and Héctor M. Pérez. "Multichannel Active Noise Control Based on Filtered-x Affine Projection-Like and LMS Algorithms with Switching Filter Selection." Applied Sciences 9, no. 21 (November 1, 2019): 4669. http://dx.doi.org/10.3390/app9214669.

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Affine projection (AP) algorithms have been demonstrated to have faster convergence speeds than the conventional least mean square (LMS) algorithms. However, LMS algorithms exhibit smaller steady-state mean square errors (MSEs) when compared with affine projection (AP) algorithms. Recently, several authors have proposed alternative methods based on convex combinations to improve the steady-state MSE of AP algorithms, even with the increased computational cost from the simultaneous use of two filters. In this paper, we present an alternative method based on an affine projection-like (APL-I) algorithm and least mean square (LMS) algorithm to solve the ANC under stationary Gaussian noise environments. In particular, we propose a switching filter selection criteria to improve the steady-state MSE without increasing the computational cost when compared with existing models. Here, we validate the proposed strategy in a single and a multichannel system, with and without automatically adjusting the scaling factor of the APL-I algorithm. The results demonstrate that the proposed scheme exploits the best features of each filter (APL-I and LMS) to guarantee rapid convergence with a low steady-state MSE. Additionally, the proposed approach demands a low computational burden compared with existing convex combination approaches, which will potentially lead to the development of real-time ANC applications.

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36

Das, Debi Prasad, Danielle J. Moreau, and Ben S. Cazzolato. "A computationally efficient frequency-domain filtered-X LMS algorithm for virtual microphone." Mechanical Systems and Signal Processing 37, no. 1-2 (May 2013): 440–54. http://dx.doi.org/10.1016/j.ymssp.2012.12.005.

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37

Yang, Feiran, Jianfeng Guo, and Jun Yang. "Stochastic Analysis of the Filtered-x LMS Algorithm for Active Noise Control." IEEE/ACM Transactions on Audio, Speech, and Language Processing 28 (2020): 2252–66. http://dx.doi.org/10.1109/taslp.2020.3012056.

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38

Gupta, A., S. Yandamuri, and S. M. Kuo. "Active vibration control of a structure by implementing filtered-X LMS algorithm." Noise Control Engineering Journal 54, no. 6 (2006): 396. http://dx.doi.org/10.3397/1.2397288.

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39

Paschedag, J., and B. Lohmann. "Error Convergence of the Filtered-X LMS Algorithm for Multiple Harmonic Excitation." IEEE Transactions on Audio, Speech, and Language Processing 16, no. 5 (July 2008): 989–99. http://dx.doi.org/10.1109/tasl.2008.924147.

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40

Ardekani, Iman Tabatabaei, and Waleed H. Abdulla. "Stochastic modelling and analysis of filtered‐x least‐mean‐square adaptation algorithm." IET Signal Processing 7, no. 6 (August 2013): 486–96. http://dx.doi.org/10.1049/iet-spr.2012.0090.

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41

Huynh, Tuan Van, Phuong Huu Nguyen, and Long Ngoc Nguyen. "ADAPTIVE NEURAL NETWORK FOR FEEDBACK ACTIVE NOISE CONTROL SYSTEM." Science and Technology Development Journal 12, no. 12 (June 28, 2009): 86–93. http://dx.doi.org/10.32508/stdj.v12i12.2323.

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This paper presents a neural-based filtered-X least-mean-square algorithm (NFXLMS) active noise control (ANC) system. The saturation of the power amplifier in ANC system is considered. A method for compensating the saturation is proposed. On line dynamic learning algorithms based on the error gradient descent method is carried out. The convergence of the algorithm is proven using a discrete Lyapunov function. Simulation results are provided for illustration.

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42

Li, Xianyu, Yulin He, and Qun Hua. "Application of Computed Tomographic Image Reconstruction Algorithms Based on Filtered Back-Projection in Diagnosis of Bone Trauma Diseases." Journal of Medical Imaging and Health Informatics 10, no. 5 (May 1, 2020): 1219–24. http://dx.doi.org/10.1166/jmihi.2020.3036.

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Objective: To improve the diagnostic rate of bone trauma diseases by applying image reconstruction algorithm based on filtered back-projection to CT images of bone trauma. Methods: Sixty-three patients with bone trauma in our hospital were selected. After hospitalization, 63 patients took satisfactory localization images to make the lesions on the localization images close to or even exceed the resolution of conventional X-ray films. After scanning, the post-processing workstation software was used for post-processing of image reconstruction algorithm based on filtered back-projection. Finally, the diagnostic accuracy of X-ray plain film, common CT image and image examination based on filtered back-projection was compared statistically. Results: Among 63 cases of bone trauma, 48 cases were found by routine CT cross-sectional examination. The image reconstruction algorithm based on filtered back-projection was applied to all cases of wrist ulnar and trauma examination. The three-dimensional imaging can display the length, direction, shape of articular surface and fracture end of bone trauma as well as the size and spatial position of free small bone fragments stereoscopically and accurately. The relationship between bone trauma and placement. Discussion: Experiments show that when the projection data are complete, the filtering back-projection algorithm can reconstruct the image better, and the overall evaluation of the new filtering function is the best. Usually, the projection data are often incomplete, sometimes even seriously insufficient. At this time, it is necessary to adopt iterative reconstruction algorithm. However, no matter which algorithm is adopted, the reconstruction speed and accuracy are improved, and the quality of the reconstructed image is improved. It remains the direction of future efforts. The FBP method is the basic common algorithm for reconstructing image, and it is also the basis of many other algorithms. It is widely used in medical CT and other fields. Conclusion: The improved CT image reconstruction algorithm based on filtered back-projection has high application value in the diagnosis of bone trauma diseases. By comparing the three indexes of serial processing time, information transfer interface and image noise, the suspicious site of bone trauma can be diagnosed clearly. In recent years, with the popularization of CT and the emergence of spiral CT, it has a good guiding significance for defining clinical diagnosis and treatment.

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43

Song, Pucha, and Haiquan Zhao. "Filtered-x least mean square/fourth (FXLMS/F) algorithm for active noise control." Mechanical Systems and Signal Processing 120 (April 2019): 69–82. http://dx.doi.org/10.1016/j.ymssp.2018.10.009.

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44

Ferrer, Miguel, Maria de Diego, Alberto Gonzalez, and Gema Piñero. "Steady-State Mean Square Performance of the Multichannel Filtered-X Affine Projection Algorithm." IEEE Transactions on Signal Processing 60, no. 6 (June 2012): 2771–85. http://dx.doi.org/10.1109/tsp.2012.2189390.

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45

Snyder, S. D., and C. H. Hansen. "The effect of transfer function estimation errors on the filtered-x LMS algorithm." IEEE Transactions on Signal Processing 42, no. 4 (April 1994): 950–53. http://dx.doi.org/10.1109/78.285659.

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46

Paillard, B., Chon Tam Le Donh, A. Berry, and J. Nicolas. "Accelerating the convergence of the filtered-x lms algorithm through transform-domain optimisation." Mechanical Systems and Signal Processing 9, no. 4 (July 1995): 445–64. http://dx.doi.org/10.1006/mssp.1995.0035.

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47

Sun, Guohua, Mingfeng Li, and Teik C. Lim. "Enhanced filtered-x least mean M-estimate algorithm for active impulsive noise control." Applied Acoustics 90 (April 2015): 31–41. http://dx.doi.org/10.1016/j.apacoust.2014.10.012.

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48

Hinamoto, Yoichi, and Hideaki Sakai. "A Filtered-X LMS Algorithm for Sinusoidal Reference Signals—Effects of Frequency Mismatch." IEEE Signal Processing Letters 14, no. 4 (April 2007): 259–62. http://dx.doi.org/10.1109/lsp.2006.884901.

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49

Wang, Yong-Qing, Fu-Chang Huang, and Hai-Bo Liu. "Adaptive filtered x-least mean square algorithm with improved convergence for resonance suppression." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 228, no. 9 (July 9, 2014): 668–76. http://dx.doi.org/10.1177/0959651814541883.

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Mondal (Das), Kuheli, Saurav Das, Aminudin Bin Hj Abu, Nozomu Hamada, Hoong Thiam Toh, Saikat Das, and Waleed Fekry Faris. "All-pass filtered x least mean square algorithm for narrowband active noise control." Applied Acoustics 142 (December 2018): 1–10. http://dx.doi.org/10.1016/j.apacoust.2018.07.026.

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