Improved training of neural networks for the nonlinear active control of sound and vibration

IEEE Transactions on Neural Networks

Volumn 10, Issue 2, 1999, Pages 391-401

  Bouchard, Martin ^a,c   Paillard, Bruno ^b   Le Dinh, Chon Tan ^b  

^a UNIVERSITY OF OTTAWA   (Canada)

^b UNIVERSITÉ DE SHERBROOKE   (Canada)

^c IEEE   (Canada)

Author keywords

Active control of sound and vibration; Alternative backpropagation schemes; Nonlinear actuators; Nonlinear control; Nonlinear recursive least squares learning algorithms

Indexed keywords

ACOUSTIC VARIABLES CONTROL; ACTUATORS; BACKPROPAGATION; CONTROL SYSTEM ANALYSIS; HEURISTIC METHODS; INTELLIGENT CONTROL; LEARNING ALGORITHMS; LEARNING SYSTEMS; LEAST SQUARES APPROXIMATIONS; NONLINEAR CONTROL SYSTEMS; VIBRATION CONTROL;

NONLINEAR ACTIVE CONTROL; NONLINEAR RECURSIVE-LEAST-SQUARES LEARNING ALGORITHMS;

MULTILAYER NEURAL NETWORKS;

EID: 0033097386     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/72.750568     Document Type: Article

References (24)

1. S. J. Elliott and P. A. Nelson, "Active noise control," IEEE Signal Processing Mag., vol. 10, pp. 12-35, 1993.
2. P. A. Nelson and S. J. Elliott, Active Control of Sound. London, U.K.: Academic, 1992.
3. M. O. Tokhi and R. R. Leitch, Active Noise Control. Oxford, U.K.: Clarendon, 1992.
4. E. F. Berkman and E. K. Bender, "Perspectives on active noise and vibration control," Sound and Vibration, vol. 31, pp. 80-94, 1997.
5. C. R. Fuller, S. J. Elliott, and P. A. Nelson, Active Control of Vibration. London, U.K.: Academic, 1996.
6. S. M. Kuo and D. R. Morgan, Active Noise Control Systems: Algorithms and DSP Implementations. New York: Wiley, 1996.
7. B. Widrow and S. D. Stearns, Adaptive Signal Processing. Englewood Cliffs, NJ: Prentice-Hall, 1985.
8. S. Haykin, Adaptive Filter Theory, 3rd ed. Englewood Cliffs, NJ: Prentice-Hall, 1996.
9. S. J. Elliott, I. M. Stothers, and P. A. Nelson, "A multiple error LMS algorithm and its application to the active control of sound and vibration," IEEE Trans. Acoust., Speech, Signal Processing, vol. 35, pp. 1423-1434, 1987.
10. Q. Shen and A. Spanias, "Frequency-domain adaptive algorithms for multichannel active sound control," in Proc. 2nd Conf. Recent Advances in Active Control of Sound and Vibrations, Blacksburg, VA, 1993, pp. 755-766.
11. M. Bouchard and B. Paillard, "A transform domain optimization to increase the convergence speed of the multichannel filtered-X LMS algorithm," J. Acoust. Soc. Amer., vol. 100, pp. 3203-3214, 1996.
12. T. Auspitzer, D. Guicking, and S. J. Elliott, "Using a fast-recursive-least-squared algorithm in a feedback-controller," in Proc. IEEE ASSP Workshop Applicat. Signal Processing Audio Acoust.s, Mohonk Mountain House, NY, 1995.
13. Y. C. Park and S. D. Sommerfeldt, "A fast adaptive noise control algorithm based on the lattice structure," Appl. Acoust., vol. 47, pp. 1-25, 1996.
14. I. S. Kim, H. S. Na, K. J. Kim, and Y. Park, "Constraint filtered-x and filtered-u least-mean-square algorithms for the active control of noise in ducts," J. Acoust. Soc. Amer., vol. 95, pp. 3379-3389, 1994.
15. S. Douglas, "Fast, exact filtered-x LMS and LMS algorithms for multichannel active noise control," in Proc. ICASSP-97, Munich, Germany, 1997, pp. 399-402.
16. L. J. Eriksson, "Development of the filtered-u algorithm for active noise control," J. Acoust. Soc. Amer., vol. 89, pp. 257-265, 1991.
17. L. J. Eriksson, M. C. Allie, and R. A. Greiner, "The selection and application of an IIR adaptive filter for use in active sound attenuation," IEEE Trans. Acoust., Speech, Signal Processing, vol. 35, pp. 433-437, 1987.
18. S. D. Snyder and N. Tanaka, "Active control of vibration using a neural network," IEEE Trans. Neural Networks, vol. 6, pp. 819-828, 1995.
19. M. O. Tokhi and R. Wood, "Active noise control using radial basis function networks," Contr. Eng. Practice, vol. 5, pp. 1311-1322, 1997.
20. S. Haykin, Neural Networks: A Comprehensive Foundation. Englewood Cliffs, NJ: Macmillan, 1994.
21. S. Shah, F. Palmieri, and M. Datum, "Optimal filtering algorithms for fast learning in feedforward neural networks," Neural Networks, vol. 5, pp. 779-787, 1992.
22. M. Bouchard, Développement et Validation d'Algorithmes à Convergence Rapide Pour l'Apprentissage de Réseaux de Neurones Utilisés en Contrôle Actif Nonlinéaire, Ph.D. dissertation, Université de Sherbrooke, Canada, 1997.
23. E. A. Wan, "Temporal backpropagation for FIR neural networks," in Proc. IEEE Int. Joint Conf. Neural Networks, San Diego, CA, vol. 1, pp. 575-580, 1990.
24. _, "Adjoint-LMS: An efficient alternative to the filtered-X LMS and multiple error LMS algorithms," in Proc. ICASSP 96, vol. 3, pp. 1842-1845, 1996.