Skip to main content
EURASIP Journal on Advances in Signal Processing
Download PDF
  • Research Article
  • Open access
  • Published:

Duct Modeling Using the Generalized RBF Neural Network for Active Cancellation of Variable Frequency Narrow Band Noise

EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 041679 (2006) Cite this article

Abstract

We have shown that duct modeling using the generalized RBF neural network (DM_RBF), which has the capability of modeling the nonlinear behavior, can suppress a variable-frequency narrow band noise of a duct more efficiently than an FX-LMS algorithm. In our method (DM_RBF), at first the duct is identified using a generalized RBF network, after that stage of time delay of the input signal to the generalized RBF network is applied, then a linear combiner at their outputs makes an online identification of the nonlinear system. The weights of linear combiner are updated by the normalized LMS algorithm. We have showed that the proposed method is more than three times faster in comparison with the FX-LMS algorithm with 30% lower error. Also the DM_RBF method will converge in changing the input frequency, while it makes the FX-LMS cause divergence.

References

  1. Kuo SM, Morgan DR: Active noise control: a tutorial review. Proceedings of the IEEE 1999,87(6):943–973. 10.1109/5.763310

    Article  Google Scholar 

  2. Eriksson LJ, Allie MC, Greiner RA: The selection and application of an IIR adaptive filter for use in active sound attenuation. IEEE Transactions on Acoustics, Speech, and Signal Processing 1987,35(4):433–437. 10.1109/TASSP.1987.1165165

    Article  Google Scholar 

  3. Eriksson LJ, Allie MC: System considerations for adaptive modelling applied to active noise control. Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS '88), June 1988, Espoo, Finland 3: 2387–2390.

    Google Scholar 

  4. Bouchard M, Feng Y: Inverse structure for active noise control and combined active noise control/sound reproduction systems. IEEE Transactions on Speech and Audio Processing 2001,9(2):141–151. 10.1109/89.902280

    Article  Google Scholar 

  5. Elliott SJ, Nelson PA: Active noise control. IEEE Signal Processing Magazine 1993,10(4):12–35. 10.1109/79.248551

    Article  Google Scholar 

  6. Morgan DR: An analysis of multiple correlation cancellation loops with a filter in the auxiliary path. IEEE Transactions on Acoustics, Speech, and Signal Processing 1980,28(4):454–467. 10.1109/TASSP.1980.1163430

    Article  Google Scholar 

  7. Burgess JC: Active adaptive sound control in a duct: a computer simulation. Journal of the Acoustical Society of America 1981,70(3):715–726. 10.1121/1.386908

    Article  Google Scholar 

  8. Rafaely B, Carrilho J, Gardonio P: Novel active noise-reducing headset using earshell vibration control. Journal of the Acoustical Society of America 2002,112(4):1471–1481. 10.1121/1.1504469

    Article  Google Scholar 

  9. Bouchard M, Paillard B, Le Dinh CT: Improved training of neural networks for the nonlinear active control of sound and vibration. IEEE Transactions on Neural Networks 1999,10(2):391–401. 10.1109/72.750568

    Article  Google Scholar 

  10. Ngia LSH, Sjoberg JH: Efficient training of neural nets for nonlinear adaptive filtering using a recursive Levenberg-Marquardt algorithm. IEEE Transactions on Signal Processing 2000,48(7):1915–1927. 10.1109/78.847778

    Article  Google Scholar 

  11. Snyder SD, Tanaka N: Active control of vibration using a neural network. IEEE Transactions on Neural Networks 1995,6(4):819–828. 10.1109/72.392246

    Article  Google Scholar 

  12. Wong T, Lo T, Leung H, Litva J, Bosse E: Low-angle radar tracking using radial basis function neural network. IEE Proceedings F: Radar and Signal Processing 1993,140(5):323–328. 10.1049/ip-f-2.1993.0045

    Article  Google Scholar 

  13. Longinov NE: Predicting pilot look-angle with a radial basis function network. IEEE Transaction on Systems, Man, and Cybernetics 1994,24(10):1511–1518. 10.1109/21.310533

    Article  Google Scholar 

  14. Clen S: Nonlinear time series modelling and prediction using Gaussian RBF networks with enhanced clustering and RLS learning. Electronics Letters 1995,31(2):117–118. 10.1049/el:19950085

    Article  Google Scholar 

  15. Chng ES, Chen S, Mulgrew B: Gradient radial basis function networks for nonlinear and nonstationary time series prediction. IEEE Transactions on Neural Networks 1996,7(1):190–194. 10.1109/72.478403

    Article  Google Scholar 

  16. Berthold MR: A time delay radial basis function network for phoneme recognition. Proceedings of IEEE International Conference on Neural Networks, June-July 1994, Orlando, Fla, USA 7: 4470–4472, 4472a.

    Google Scholar 

  17. Ryad Z, Daniel R, Noureddine Z: The RRBF. Dynamic representation of time in radial basis function network. Proceedings of 8th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA '01), October 2001, Antibes-Juan les Pins, France 2: 737–740.

    Google Scholar 

  18. Sayyarrodsari B, How JP, Hassibi B, Carrier A: An estimation-based approach to the design of adaptive IIR filters. Proceedings of the American Control Conference, June 1998, Philadelphia, Pa, USA 5: 3148–3152.

    Google Scholar 

  19. Lveg P: Process of silencing sound oscillations. US Patent no. 2043416, June, 1936

  20. Bjarnason E: Analysis of the filtered-X LMS algorithm. IEEE Transactions on Speech and Audio Processing 1995,3(6):504–514. 10.1109/89.482218

    Article  Google Scholar 

  21. Rupp M: Saving complexity of modified filtered-X-LMS and delayed update LMS algorithms. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 1997,44(1):57–60. 10.1109/82.559371

    Article  Google Scholar 

  22. Kuo SM, Panahi I, Chung KM, Horner T, Nadeski , Chyan J: Design of active noise control systems with the TMS320 family. In Tech. Rep. SPRA042. Texas Instruments, Dallas, Tex, USA; 1996.

    Google Scholar 

  23. Phooi SK, Zhihong M, Wu HR: Nonlinear active noise control using Lyapunov theory and RBF network. Proceedings of the IEEE Workshop on Neural Networks for Signal Processing, December 2000, Sydney, NSW, Australia 2: 916–925.

    Google Scholar 

  24. Cartes DA, Ray LR, Collier RD: Lyapunov turning of the leaky LMS algorithm for single-source, single-point noise cancellation. Mechanical System and Signal Processing 2003,17(5):925–944. 10.1006/mssp.2002.1519

    Article  Google Scholar 

  25. Haykin S: Neural Networks: A Comprehensive Foundation. MacMillan College, New York, NY, USA; 1994.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Engineering Department, Tarbiat Moallem University of Sabzevar, P.O. Box 397, Sabzevar, Iran

    Hadi Sadoghi Yazdi & Javad Haddadnia

  2. Department of Electrical Engineering, Tarbiat Modarres University, P.O. Box 14115-143, Tehran, Iran

    Mojtaba Lotfizad

Authors
  1. Hadi Sadoghi Yazdi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Javad Haddadnia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mojtaba Lotfizad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hadi Sadoghi Yazdi.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Yazdi, H.S., Haddadnia, J. & Lotfizad, M. Duct Modeling Using the Generalized RBF Neural Network for Active Cancellation of Variable Frequency Narrow Band Noise. EURASIP J. Adv. Signal Process. 2007, 041679 (2006). https://doi.org/10.1155/2007/41679

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1155/2007/41679

Keywords