- Volume 16 Issue 12
DOI QR Code
Intelligent IIR Filter based Multiple-Channel ANC Systems
지능형 IIR 필터 기반 다중 채널 ANC 시스템
- Cho, Hyun-Cheol (Ulsan College) ;
- Yeo, Dae-Yeon (Dong-A University) ;
- Lee, Young-Jin (Korea Aviation Polytechnic College) ;
- Lee, Kwon-Soon (Dong-A University)
- Received : 2010.07.13
- Accepted : 2010.11.10
- Published : 2010.12.01
This paper proposes a novel active noise control (ANC) approach that uses an IIR filter and neural network techniques to effectively reduce interior noise. We construct a multiple-channel IIR filter module which is a linearly augmented framework with a generic IIR model to generate a primary control signal. A three-layer perceptron neural network is employed for establishing a secondary-path model to represent air channels among noise fields. Since the IIR module and neural network are connected in series, the output of an IIR filter is transferred forward to the neural model to generate a final ANC signal. A gradient descent optimization based learning algorithm is analytically derived for the optimal selection of the ANC parameter vectors. Moreover, re-estimation of partial parameter vectors in the ANC system is proposed for online learning. Lastly, we present the results of a numerical study to test our ANC methodology with realistic interior noise measurement obtained from Korean railway trains.
- J. C. Kim, “Survey of railway technology in advanced countries,” J. of the Korean Society for Railway, vol. 11, no. 3, pp. 23-28, 2008.
- Y. Song, Y. Gong, and M. Sen, “A robust hybrid feedback active noise cancellation headset,” IEEE Trans. on Speech & Audio Processing, vol. 13, no. 4, pp. 607-617, 2005. https://doi.org/10.1109/TSA.2005.848878
- T. Tanaka, C. Jindaiji-higashicho, and K. Li, “Development of an active muffler for medium-duty diesel vehicles considering thermal influence and control trackability,” Noise Control Engineering Journal, vol. 51, no. 2, pp. 90-96, 2003. https://doi.org/10.3397/1.2839702
- P. Menounou and E. Papaefthymiou, “Use of noise barriers for helicopter noise mitigation,” Noise and Vibration Worldwide, vol. 40, no. 5, pp. 10-21, 2009. https://doi.org/10.1260/095745609788549248
- S. Ishimitsu and S. J. Elliott, “Improvement of the convergence property of adaptive feedforward controllers and their application to the active control of ship interior noise,” Acoustical Science and Technology, vol. 25, no. 3, pp. 181-187, 2004. https://doi.org/10.1250/ast.25.181
- M. A. Botto, J. M. C. Sousa, and J. M. G. Sa da Costa, “Intelligent active noise control applied to a laboratory railway coach model,” Control Engineering Practice, vol. 13, pp. 473-484, 2005. https://doi.org/10.1016/j.conengprac.2004.04.009
- J. Conchinha, J. M. Sousa, M. A. Botto, and J. S. da Costa, “Model based active noise control using neural network,” Proc. of European Control Conf., Portugal, pp. 72-77, 2001.
- H. C. Cho, K. S. Lee, and H. D. Nam, “A neural multiple LMS based ANC system for reducing acoustic noise of high-speed trains,” KIEE, vol. 58P, no. 4, pp. 385-390, 2009.
- S. Haykin, Neural Networks and Learning Machines, Upper saddle, New Jersey, Prentice Hall, 2008.
- H. C. Cho and K. S. Lee, “Intelligent online control for nonlinear mechanical systems with random friction effect,” KIEE, vol. 56, no. 12, pp. 2226-2232, 2007.