Filtered-X Radial Basis Function Neural Networks for Active Noise Control

This paper presents active control of acoustic noise using radial basis function (RBF) networks and its digital signal processor (DSP) real-time implementation. The neural control system consists of two stages: first, identification (modeling) of secondary path of the active noise control using RBF networks and its learning algorithm, and secondly neural control of primary path based on neural model obtained in the first stage. A tapped delay line is introduced in front of controller neural, and another tapped delay line is inserted between controller neural networks and model neural networks. A new algorithm referred to as Filtered X-RBF is proposed to account for secondary path effects of the control system arising in active noise control. The resulting algorithm turns out to be the filtered-X version of the standard RBF learning algorithm. We address centralized and decentralized controller configurations and their DSP implementation is carried out. Effectiveness of the neural controller is demonstrated by applying the algorithm to active noise control within a 3 dimension enclosure to generate quiet zones around error microphones. Results of the real-time experiments show that 10-23 dB noise attenuation is produced with moderate transient response.

[1]  Colin H. Hansen,et al.  ENGINEERING NOISE CONTROL: Theory and Practice , 1988 .

[2]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[3]  Kenko Uchida,et al.  345 Active Control of Acoustic Noise Using Adaptive H_∞ Filter Algorithm , 2001 .

[4]  Scott D. Snyder,et al.  Active control of vibration using a neural network , 1995, IEEE Trans. Neural Networks.

[5]  Chris R. Fuller,et al.  Active control of sound and vibration , 1990 .

[6]  Philip A. Nelson,et al.  Active Control of Sound , 1992 .

[7]  Richard D. Braatz,et al.  On the "Identification and control of dynamical systems using neural networks" , 1997, IEEE Trans. Neural Networks.

[8]  Dennis S. Bernstein,et al.  Modeling, identification, and feedback control of noise in an acoustic duct , 1996, IEEE Trans. Control. Syst. Technol..

[9]  Martin Bouchard,et al.  Improved training of neural networks for the nonlinear active control of sound and vibration , 1999, IEEE Trans. Neural Networks.

[10]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[11]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.

[12]  Sen M. Kuo,et al.  Active Noise Control Systems: Algorithms and DSP Implementations , 1996 .

[13]  S.J. Elliott,et al.  Active noise control , 1993, IEEE Signal Processing Magazine.

[14]  R.T. Bambang,et al.  Identification of secondary path in ANC using diagonal recurrent neural networks with EKF algorithm , 2004, 2004 5th Asian Control Conference (IEEE Cat. No.04EX904).

[15]  S. J. Elliott.  Down with noise [active noise control] , 1999 .

[16]  Snehasis Mukhopadhyay,et al.  Adaptive control using neural networks and approximate models , 1997, IEEE Trans. Neural Networks.

[17]  Kumpati S. Narendra,et al.  Gradient methods for the optimization of dynamical systems containing neural networks , 1991, IEEE Trans. Neural Networks.

[18]  L. R. Quartararo,et al.  Noise and Vibration Control Engineering: Principles and Applications , 1992 .