Functional link artificial neural network filter based on the q-gradient for nonlinear active noise control

Abstract As one of the most commonly used nonlinear active noise control (NANC) algorithms, the filtered-s least mean square (FsLMS) algorithm outperforms the conventional filtered-x least mean square (FxLMS) algorithm when the primary path has a quadratic nonlinearity. However, it still suffers from performance degradation under strong interferences. In this paper, two new algorithms, named filtered-s q-least mean p-norm (FsqLMP) and filtered-s q-least mean square (FsqLMS), based on the concept of Jackson's derivative, are proposed. By using new Jackson's derivative method, the proposed algorithms are less sensitive to the interferences in NANC system. Additionally, it is shown that the family of q-least mean square algorithms are special cases of the proposed FsqLMP algorithm. To further improve performance of the FsqLMS algorithm and solve the parameter selection problem, a time varying q scheme is developed. Simulation studies indicate that the proposed algorithms provide superior performance in various noise environments as compared to the existing algorithms.

[1]  Ganapati Panda,et al.  Active control of nonlinear noise processes using cascaded adaptive nonlinear filter , 2013 .

[2]  Lu Lu,et al.  Active impulsive noise control using maximum correntropy with adaptive kernel size , 2017 .

[3]  M. Moinuddin,et al.  The q-normalized least mean square algorithm , 2016, 2016 6th International Conference on Intelligent and Advanced Systems (ICIAS).

[4]  Jinwei Sun,et al.  A modified FSLMS algorithm for nonlinear ANC , 2016, 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA).

[5]  Muhammad Moinuddin,et al.  Design of an Intelligent q-LMS Algorithm for Tracking a Non-stationary Channel , 2018 .

[6]  Peng Li,et al.  Active noise cancellation algorithms for impulsive noise. , 2013, Mechanical systems and signal processing.

[7]  Benoit Champagne,et al.  Distributed Nonlinear System Identification in $\alpha$ -Stable Noise , 2018, IEEE Signal Processing Letters.

[8]  C. L. Nikias,et al.  Signal processing with fractional lower order moments: stable processes and their applications , 1993, Proc. IEEE.

[9]  Nirmal Kumar Rout,et al.  Nonlinear feedback active noise control for broadband chaotic noise , 2014, Appl. Soft Comput..

[10]  Mohammad J. Mahjoob,et al.  Feedforward active noise control using wavelet frames: simulation and experimental results , 2017 .

[11]  Li Tan,et al.  On implementation of adaptive bilinear filters for nonlinear active noise control , 2016 .

[12]  Hua Bao,et al.  Active Noise Control based on Kernel Least-Mean-Square algorithm , 2009, 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers.

[13]  Ganapati Panda,et al.  Active mitigation of nonlinear noise Processes using a novel filtered-s LMS algorithm , 2004, IEEE Transactions on Speech and Audio Processing.

[14]  Jinwei Sun,et al.  A novel bilinear functional link neural network filter for nonlinear active noise control , 2018, Appl. Soft Comput..

[15]  Sen M. Kuo,et al.  Nonlinear adaptive bilinear filters for active noise control systems , 2005, IEEE Transactions on Circuits and Systems I: Regular Papers.

[16]  W. Mitsuhashi,et al.  Improving performance of FxLMS algorithm for active noise control of impulsive noise , 2009 .

[17]  L. Piroddi,et al.  Active noise control with on-line estimation of non-Gaussian noise characteristics , 2012 .

[18]  Li Tan,et al.  Adaptive Volterra filters for active control of nonlinear noise processes , 2001, IEEE Trans. Signal Process..

[19]  Tyseer Aboulnasr,et al.  A robust variable step-size LMS-type algorithm: analysis and simulations , 1997, IEEE Trans. Signal Process..

[20]  Nithin V. George,et al.  Compensating acoustic feedback in feed-forward active noise control systems using spline adaptive filters , 2016, Signal Process..

[21]  Lifu Wu,et al.  An Active Impulsive Noise Control Algorithm With Logarithmic Transformation , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[22]  Ganapati Panda,et al.  Advances in active noise control: A survey, with emphasis on recent nonlinear techniques , 2013, Signal Process..

[23]  Dennis R. Morgan,et al.  History, applications, and subsequent development of the FXLMS Algorithm [DSP History] , 2013, IEEE Signal Processing Magazine.

[24]  G. L. Sicuranza,et al.  A Generalized FLANN Filter for Nonlinear Active Noise Control , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[25]  Debi Prasad Das,et al.  Fast exact multichannel FSLMS algorithm for active noise control , 2009, Signal Process..

[26]  T. Lim,et al.  A family of threshold based robust adaptive algorithms for active impulsive noise control , 2015 .

[27]  Haiquan Zhao,et al.  Adaptive Volterra filter with continuous lp-norm using a logarithmic cost for nonlinear active noise control , 2016 .

[28]  Debi Prasad Das,et al.  Fast Adaptive Algorithms for Active Control of Nonlinear Noise Processes , 2008, IEEE Transactions on Signal Processing.

[29]  Milad Tatari,et al.  Active noise control using adaptive POLYnominal Gaussian WinOwed wavelet networks , 2015 .

[30]  Nithin V. George,et al.  Nonlinear active noise control using spline adaptive filters , 2015 .

[31]  Z. Qiu,et al.  A multi-resolution filtered-x LMS algorithm based on discrete wavelet transform for active noise control , 2016 .

[32]  John J. Shynk,et al.  Analysis of the momentum LMS algorithm , 1990, IEEE Trans. Acoust. Speech Signal Process..

[33]  Dinh Cong Le,et al.  A bilinear functional link artificial neural network filter for nonlinear active noise control and its stability condition , 2018 .

[34]  Giovanni L. Sicuranza,et al.  Filtered-X affine projection algorithm for multichannel active noise control using second-order Volterra filters , 2004, IEEE Signal Processing Letters.

[35]  Sen M. Kuo,et al.  Active Noise Control Systems: Algorithms and DSP Implementations , 1996 .

[36]  Xu Sun,et al.  Adaptive algorithm for active control of impulsive noise , 2006 .

[37]  Yang Li,et al.  Sparse Modeling of Nonlinear Secondary Path for Nonlinear Active Noise Control , 2018, IEEE Transactions on Instrumentation and Measurement.

[38]  Li Tan,et al.  Active control of impulsive noise using a nonlinear companding function , 2015 .

[39]  Cheng-Yuan Chang,et al.  Enhancement of active noise control using neural-based filtered-X algorithm , 2007 .

[40]  G. Panda,et al.  A reduced complexity adaptive legendre neural network for nonlinear active noise control , 2012, 2012 19th International Conference on Systems, Signals and Image Processing (IWSSIP).

[41]  Giovanni L. Sicuranza,et al.  Piecewise-Linear Expansions for Nonlinear Active Noise Control , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[42]  Yuqi Liu,et al.  Kernel Filtered-x LMS Algorithm for Active Noise Control System with Nonlinear Primary Path , 2018, Circuits Syst. Signal Process..

[43]  R. Leahy,et al.  Adaptive filtering of stable processes for active attenuation of impulsive noise , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.

[44]  Ganapati Panda,et al.  A robust filtered-s LMS algorithm for nonlinear active noise control , 2012 .

[45]  Muhammad Arif,et al.  The q-Least Mean Squares algorithm , 2015, Signal Process..

[46]  Martin Bouchard,et al.  New recursive-least-squares algorithms for nonlinear active control of sound and vibration using neural networks , 2001, IEEE Trans. Neural Networks.