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Performance Evaluation of Particle Swarm Optimization Based Active Noise Control Algorithm

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Swarm, Evolutionary, and Memetic Computing (SEMCCO 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6466))

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Abstract

Active noise control (ANC) has been used to control low-frequency acoustic noise. The ANC uses an adaptive filter algorithm and normally uses least mean square (LMS) algorithm. The gradient based LMS algorithm suffers from local minima problem. In this paper, particle swarm optimization (PSO) algorithm, which is a non-gradient but simple evolutionary computing type algorithm, is proposed for the ANC system. Detailed mathematical treatment is made and systematic computer simulation studies are carried out to evaluate the performance of the PSO based ANC algorithm.

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References

  1. Kuo, S.M., Morgan, D.R.: Active Noise Control Systems—Algorithms and DSP Implementations. Wiley, New York (1996)

    Google Scholar 

  2. Das, D.P., Panda, G.: Active Mitigation of Nonlinear Noise Processes using a novel filtered-s LMS Algorithm. IEEE Trans. Speech and Audio Process. 12(3), 313–322 (2004)

    Article  Google Scholar 

  3. Russo, F., Sicuranza, G.L.: Accuracy and Performance Evaluation in the Genetic Optimization of Nonlinear Systems for Active Noise Control. IEEE Trans. Instrum. Meas. 56(4), 1443–1450 (2007)

    Article  Google Scholar 

  4. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc. of IEEE Int. Conf. Neural Networks, pp. 1942–1948 (1995)

    Google Scholar 

  5. Modares, H., Ahmadyfard, A., Hadadzarif, M.: A PSO approach for non-linear active noise cancellation. In: Proc. the 6th WSEAS International Conference on Simulation, Modelling and Optimization, Lisbon, Portugal, pp. 492–497 (2006)

    Google Scholar 

  6. Liang, J.J., Suganthan, P.N.: Dynamic Multi-Swarm Particle Swarm Optimizer. In: IEEE Swarm Intelligence Symposium, pp. 124–129 (2005)

    Google Scholar 

  7. Liang, J.J., Qin, A.K., Suganthan, P.N., Baskar, S.: Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions. IEEE Trans. on Evolutionary Computation 10(3), 281–295 (2006)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Electronics Engineering, KIIT University, Bhubaneswar, India

    Nirmal Kumar Rout

  2. PE&I Cell, IMMT (CSIR), Bhubaneswar, India

    Debi Prasad Das

  3. School of Electrical Sciences, IIT, Bhubaneswar, India

    Ganapati Panda

Authors
  1. Nirmal Kumar Rout
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  2. Debi Prasad Das
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  3. Ganapati Panda
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology, New Delhi, India

    Bijaya Ketan Panigrahi

  2. Department of Electronics and Communication Engineering, Jadavpur University, 700032, Kolkata, West Bengal, India

    Swagatam Das

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore

    Ponnuthurai Nagaratnam Suganthan

  4. Department of Electrical and Electronics Engineering, SRM University, Chennai, Tamil Nadu, India

    Subhransu Sekhar Dash

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© 2010 Springer-Verlag Berlin Heidelberg

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Rout, N.K., Das, D.P., Panda, G. (2010). Performance Evaluation of Particle Swarm Optimization Based Active Noise Control Algorithm. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2010. Lecture Notes in Computer Science, vol 6466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17563-3_62

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  • DOI: https://doi.org/10.1007/978-3-642-17563-3_62

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17562-6

  • Online ISBN: 978-3-642-17563-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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