Abstract
Evolutionary-computing-algorithm-based nonlinear active noise control (ANC) removes the requirement of secondary path modeling, which is essential for proper functioning of a conventional gradient-descent-approach based ANC system. However, the noise mitigation capability of such algorithms is largely dependent on the proper selection of the agent count as well as on the number of sound samples processed by an agent in a given iteration. In order to alleviate this dependency, we propose a dynamic nonlinear ANC (DNANC) system, which adapts its parameters in accordance with the acoustic scenario under consideration. The nonlinear ANC (NANC) problem has been formulated as a multi-objective optimization problem in this chapter. We have used the non-domination sorting genetic algorithm II (NSGA-II) for solving the optimization task. The conflicting objectives employed in this chapter are the ensemble mean-square error and the computation time. The proposed DNANC system has been shown to adapt itself to several ANC scenarios in a dynamic manner, wherein, the controller structure has been optimized for the situation considered.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akhtar MT, Abe M, Kawamata M (2005) A new structure for feed forward active noise control systems with improved online secondary path modeling. IEEE Trans Speech Audio Proc 13:1082–1088
Akhtar MT, Abe M, Kawamata M (2006) A new variable step size LMS algorithm-based method for improved online secondary path modeling in active noise control systems. IEEE Trans Audio Speech Lang Process 14:720–726
Ardekani IT, Abdulla WH (2012) Effects of imperfect secondary path modeling on adaptive active noise control systems. IEEE Trans Control Syst Technol 20:1252–1262
Aslam MS, Raja MAZ (2015) A new adaptive strategy to improve online secondary path modeling in active noise control systems using fractional signal processing approach. Signal Process 107:433–443
Behera SB, Das DP, Rout NK (2014) Nonlinear feedback active noise control for broadband chaotic noise. Appl Soft Comput 15:80–87
Chang CY, Chen DR (2010) Active noise cancellation without secondary path identification by using an adaptive genetic algorithm. IEEE Trans Actions Instrum Measur 59:2315–2327
Das DP, Panda G (2004) Active mitigation of nonlinear noise processes using a novel filtered-s LMS algorithm. IEEE Trans Speech Audio Proc 12:313–322
Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Chichester
Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182–197
Eriksson LJ, Allie MC (1989) Use of random noise for on-line transducer modeling in an adaptive active attenuation system. J Acoust Soc Am 85:797–802
George NV, Panda G (2012a) A particle swarm optimization based de- centralized nonlinear active noise control system. IEEE Trans Instrum Meas 61:3378–3386
George NV, Panda G (2012b) A robust filtered-s LMS algorithm for nonlinear active noise control. Appl Acoust 73:836–841
Gholami-Boroujeny S, Eshghi M (2012) Non-linear active noise cancellation using a bacterial foraging optimisation algorithm. IET Signal Proc 6:364–373
Kuo SM, Morgan DR (1999) Active noise control: a tutorial review. Proc IEEE 87:943–975
Patel V, Gandhi V, Heda S, George NV (2016) Design of adaptive exponential functional link network-based nonlinear filters. IEEE Trans Circuit Syst I Regul Papers 63:1434–1442
Rout NK, Das DP, Panda G (2011) Particle swarm optimization based active noise control algorithm without secondary path identification. IEEE Trans Instrum Meas 61:554–563
Rout NK, Das DP, Panda G (2016) Particle swarm optimization based nonlinear active noise control under saturation nonlinearity. Appl Soft Comput 41:275–289
Sicuranza GL, Carini A (2011) A generalized FLANN filter for nonlinear active noise control. IEEE Trans Audio Speech Lang Process 19:2412–2417
Spiriti E, Morici S, Piroddi L (2014) A gradient-free adaptation method for nonlinear active noise control. J Sound Vib 333:13–30
Storn R, Price K (1997) Differential evolution-A simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359
Tan LZ, Jiang J (1997) Filtered-X second-order Volterra adaptive algorithms. Electron Lett 33:671–672
Tyagi S, Katre V, George NV (2014) Online estimation of secondary path in active noise control systems using Generalized Levinson Durbin algorithm. In: Proceedings of the 19th international conference on digital signal processing, pp 552–555
Yang XS, Deb S (2009) Cuckoo search via L´evy flights. In: Proceedings of IEEE world congress on nature & biologically inspired computing, pp 210–214
Zhang M, Lan H, Ser W (2001) Cross-updated active noise control system with online secondary path modeling. IEEE Trans Speech Audio Proc 9:598–602
Zhang M, Lan H, Ser W (2005) On comparison of online secondary path modeling methods with auxiliary noise. IEEE Trans Speech Audio Proc 13:618–628
Zhao H, Zeng X, Zhang J (2010) Adaptive reduced feedback FLNN filter for active control of nonlinear noise processes. Signal Process 90:834–847
Acknowledgements
This work was supported by the Department of Science and Technology, Government of India under the Fast Track Scheme for Young Scientists (SERB/ET-0018/2013).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Patwardhan, A.P., Patidar, R., George, N.V. (2020). Dynamic Nonlinear Active Noise Control: A Multi-objective Evolutionary Computing Approach. In: Bennis, F., Bhattacharjya, R. (eds) Nature-Inspired Methods for Metaheuristics Optimization. Modeling and Optimization in Science and Technologies, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-030-26458-1_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-26458-1_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26457-4
Online ISBN: 978-3-030-26458-1
eBook Packages: EngineeringEngineering (R0)