Model-based active noise control using neural networks

2001 ◽  
Author(s):  
J. M. Concinnila ◽  
J. M. Sousa ◽  
M. Ayala Botto ◽  
J. Sa da Costa
Keyword(s):  
Neural Networks ◽  
Noise Control ◽  
Active Noise Control ◽  
Model Based ◽  
Active Noise

Active Noise Control Using Multi-Layered Perceptron Neural Networks

1997 ◽  
Vol 16 (2) ◽  
pp. 109-144 ◽  
Author(s):  
M.O. Tokhi ◽  
R. Wood
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Noise Control ◽  
Learning Algorithm ◽  
Free Field ◽  
Active Noise Control ◽  
Three Dimensional ◽  
Broadband Noise ◽  
Active Noise

This paper presents the development of a neuro-adaptive active noise control (ANC) system. Multi-layered perceptron neural networks with a backpropagation learning algorithm are considered in both the modelling and control contexts. The capabilities of the neural network in modelling dynamical systems are investigated. A feedforward ANC structure is considered for optimum cancellation of broadband noise in a three-dimensional propagation medium. An on-line adaptation and training mechanism allowing a neural network architecture to characterise the optimal controller within the ANC system is developed. The neuro-adaptive ANC algorithm thus developed is implemented within a free-field environment and simulation results verifying its performance are presented and discussed.

Nonlinear Active Noise Control via Model-Based Approaches

2011 ◽  
Vol 15 (7) ◽  
pp. 854-859
Author(s):  
Sam Chau Duong ◽  
◽  
Hiroshi Kinjo ◽  
Naoki Oshiro
Keyword(s):  
Noise Control ◽  
Active Noise Control ◽  
Computational Cost ◽  
Lms Algorithm ◽  
Road Traffic Noise ◽  
Backpropagation Algorithm ◽  
Time Step ◽  
Research Attention ◽  
Model Based ◽  
Active Noise

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.

Adaptive recurrent fuzzy neural networks for active noise control

2006 ◽  
Vol 296 (4-5) ◽  
pp. 935-948 ◽  
Author(s):  
Qi-Zhi Zhang ◽  
Woon-Seng Gan ◽  
Ya-li Zhou
Keyword(s):  
Neural Networks ◽  
Noise Control ◽  
Active Noise Control ◽  
Fuzzy Neural Networks ◽  
Active Noise ◽  
Fuzzy Neural
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