An efficient algorithm for impulsive active noise control using maximum correntropy with conjugate gradient

2022 ◽  
Vol 188 ◽  
pp. 108511
Author(s):  
E.L. Zhou ◽  
B.Y. Xia ◽  
Eric Li ◽  
T.T. Wang
Keyword(s):  
Conjugate Gradient ◽  
Efficient Algorithm ◽  
Noise Control ◽  
Active Noise Control ◽  
Active Noise

Evaluating the Performance of Training Algorithms in Active Noise Control Using MLP Neural Network

2012 ◽  
Vol 468-471 ◽  
pp. 1613-1617
Author(s):  
Mehrshad Salmasi ◽  
Homayoun Mahdavi-Nasab
Keyword(s):  
Neural Network ◽  
Conjugate Gradient ◽  
Noise Control ◽  
Active Noise Control ◽  
Back Propagation ◽  
Training Algorithms ◽  
Mlp Neural Network ◽  
Scaled Conjugate Gradient ◽  
Active Noise ◽  
Levenberg Marquardt

Passive methods are costly and ineffective in noise reduction at low frequencies. Active noise control has been suggested because of these problems. Active noise control (ANC) is based on the destructive interference between the noise source waves and a controlled secondary source. In this paper, various training algorithms are compared in active cancellation of modeled sound noise using MLP neural network. Colored noise signals are used as a model of sound noise instead of noise signals from databases. An MLP neural network with different architectures is used in simulation procedure. The effect of number of neurons on the convergence speed of various training algorithms is investigated in this paper. Levenberg-Marquardt (LM), scaled conjugate gradient (SCG), BFGS quasi-Newton (BFG), resilient back-propagation (RP) and variable learning rate back-propagation (GDX) are used for training the network. Simulation results show that Levenberg-Marquardt (LM) and scaled conjugate gradient (SCG) are the fastest training algorithms.

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