JACIII Vol.15 No.7 pp. 854-859
doi: 10.20965/jaciii.2011.p0854


Nonlinear Active Noise Control via Model-Based Approaches

Sam Chau Duong, Hiroshi Kinjo, and Naoki Oshiro

Faculty of Engineering, University of the Ryukyus, 1 Senbaru, Nishihara, Okinawa 903-0213, Japan

March 12, 2011
May 9, 2011
September 20, 2011
active noise control, model-based signal processing, neural network, engine noise control, traffic noise control
Active noise control has attracted much research attention due to its several advantages over passive noise control. This paper introduces two model-based noise canceling techniques, that is, using the Moving Average (MA) model and a feedforward Neural Network (NN) to estimate the signal. The Least Mean Square (LMS) algorithm is used to minimize the error in the MA model while a backpropagation algorithm is employed to optimize the NN. Due to its advantages of good robustness and nonlinear processing, the NN is considered to be suitable for nonlinear signals. In order to reduce computational cost, the backpropagation algorithm in the NN is applied once at each time step with only one iteration. To examine the methods, two real-world problems are considered, one being engine noise and the other road traffic noise. A comparison between the two methods is carried out. Results indicate that both the MA and NN processors are effective in reducing the noises and that the NN based approach is superior over the MA model, especially for low frequency band.
Cite this article as:
S. Duong, H. Kinjo, and N. Oshiro, “Nonlinear Active Noise Control via Model-Based Approaches,” J. Adv. Comput. Intell. Intell. Inform., Vol.15 No.7, pp. 854-859, 2011.
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