Hierarchical partial update generalized functional link artificial neural network filter for nonlinear active noise control

Abstract To reduce the computational burden of the generalized FLANN (GFLANN) filter for nonlinear active noise control (NANC), a hierarchical partial update GFLANN (HPU-GFLANN) filter is presented in this paper. Based on the principle of divide and conquer, the proposed HPU-GFLANN divides the complex GFLANN filter (i.e., long memory length and large cross-terms selection parameter) into simple small-scale GFLANN modules and then interconnected in a pipelined form. Since those modules are simultaneously performed in a parallelism fashion, there is a significant improvement in computational efficiency. Besides, a hierarchical learning strategy is used to avoid the coupling effect between the nonlinear and linear part of the pipelined architecture. Data-dependent hierarchical M-Max filtered-error LMS algorithm is derived to selectively update coefficients of the HPU-GFLANN filter, which can further reduce the computational complexity. Moreover, the convergence analysis of the NANC system indicates that the proposed algorithm is stable. Computer simulation results verify that the proposed adaptive HPU-GFLANN filter is more effective in nonlinear ANC systems than the FLANN and GFLANN filters.

[1]  Tyseer Aboulnasr,et al.  Complexity reduction of the NLMS algorithm via selective coefficient update , 1999, IEEE Trans. Signal Process..

[2]  Ioannis B. Theocharis,et al.  Pipelined Recurrent Fuzzy Neural Networks for Nonlinear Adaptive Speech Prediction , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[3]  Paul Strauch,et al.  Active control of nonlinear noise processes in a linear duct , 1998, IEEE Trans. Signal Process..

[4]  Ying-Ren Chien,et al.  A new variable step-size method for the M-max LMS algorithms , 2014, 2014 IEEE International Conference on Consumer Electronics - Taiwan.

[5]  Nader Sadegh,et al.  A perceptron network for functional identification and control of nonlinear systems , 1993, IEEE Trans. Neural Networks.

[6]  Debi Prasad Das,et al.  Adaptive nonlinear active noise control algorithm for active headrest with moving error microphones , 2017 .

[7]  Giovanni L. Sicuranza,et al.  On the BIBO Stability Condition of Adaptive Recursive FLANN Filters With Application to Nonlinear Active Noise Control , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

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

[9]  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.

[10]  Riyanto T. Bambang.  Adjoint EKF learning in recurrent neural networks for nonlinear active noise control , 2008, Appl. Soft Comput..

[11]  José Carlos M. Bermudez,et al.  Stochastic analysis of the filtered-X LMS algorithm in systems with nonlinear secondary paths , 2002, IEEE Trans. Signal Process..

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

[13]  Nithin V. George,et al.  Convex combination of nonlinear adaptive filters for active noise control , 2014 .

[14]  Dayong Zhou,et al.  Efficient Adaptive Nonlinear Filters for Nonlinear Active Noise Control , 2007, IEEE Transactions on Circuits and Systems I: Regular Papers.

[15]  Tomasz Krukowicz,et al.  Neural fixed-parameter active noise controller for variable frequency tonal noise , 2013, Neurocomputing.

[16]  Nithin V. George,et al.  Design of Adaptive Exponential Functional Link Network-Based Nonlinear Filters , 2016, IEEE Transactions on Circuits and Systems I: Regular Papers.

[17]  Xiangping Zeng,et al.  Adaptive reduced feedback FLNN filter for active control of nonlinear noise processes , 2010, Signal Process..

[18]  Sheng Zhang,et al.  Pipelined set-membership approach to adaptive Volterra filtering , 2016, Signal Process..

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

[20]  Jiashu Zhang,et al.  Pipelined robust M-estimate adaptive second-order Volterra filter against impulsive noise , 2014, Digit. Signal Process..

[21]  Moncef Benkherrat,et al.  Partial-Update Adaptive Filters for Event-Related Potentials Denoising , 2017 .

[22]  Ganapati Panda,et al.  On the development of a partial update multichannel nonlinear active noise control system , 2013, 2013, 7th International Conference on Signal Processing and Communication Systems (ICSPCS).

[23]  Jiashu Zhang,et al.  A Novel Adaptive Nonlinear Filter-Based Pipelined Feedforward Second-Order Volterra Architecture , 2009, IEEE Transactions on Signal Processing.

[24]  Jiashu Zhang,et al.  A hierarchical alternative updated adaptive Volterra filter with pipelined architecture , 2016, Digit. Signal Process..

[25]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[26]  Jiashu Zhang,et al.  A novel adaptive bilinear filter based on pipelined architecture , 2010, Digit. Signal Process..

[27]  Kutluyil Dogancay,et al.  Partial-Update Adaptive Signal Processing: Design Analysis and Implementation , 2008 .

[28]  Khaled A. Mayyas.  Performance analysis of the selective coefficient update NLMS algorithm in an undermodeling situation , 2013, Digit. Signal Process..

[29]  Cheng-Yuan Chang.  Neural filtered-U algorithm for the application of active noise control system with correction terms momentum , 2010, Digit. Signal Process..

[30]  Nithin V. George,et al.  Partial update even mirror fourier non-linear filters for active noise control , 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).

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

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

[33]  W. Gan,et al.  Adaptive recurrent fuzzy neural networks for active noise control , 2006 .

[34]  Liang Li,et al.  Nonlinear adaptive prediction of nonstationary signals , 1995, IEEE Trans. Signal Process..

[35]  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.

[36]  Sheng Zhang,et al.  A generalized exponential functional link artificial neural networks filter with channel-reduced diagonal structure for nonlinear active noise control , 2018, Applied Acoustics.

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

[38]  Ganapati Panda,et al.  On the development of adaptive hybrid active noise control system for effective mitigation of nonlinear noise , 2012, Signal Process..