Optimization of Secondary Path Identification in Active Noise Control Based on Neural Network
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摘要: 针对有源噪声控制中非线性因素影响建模精度和控制效果的问题，采用神经网络代替传统模型，推导对应的控制算法。用训练结果验证了神经网络对次级通道辨识模型精度的提高。以管道为实验对象，搭建有源噪声控制实验平台，进行噪声控制实验，将传统次级通道模型与优化次级通道模型的实验结果进行对比。结果表明：在低频条件下，针对单一频率和两种频率混合的噪声源，相比传统模型和算法，神经网络优化模型和算法取得了较好的效果。Abstract: The modeling accuracy and control resulting in active noise control are affected by the nonlinear factors. The secondary path identification is optimized according to the active noise control (ANC) principle, to improve the accuracy and effect of noise control. The finite impulse response (FIR) model used to identify is replaced by the back propagation (BP) neural network, which performs better on the nonlinear factors. Based on least mean square (LMS) algorithm, the ANC algorithm under the secondary path model of neural network is deduced, and the iteration formula of coefficients is derived. The active noise control platform in a duct is built with the TMS320VC5509A as the core processor and the duct as the noise environment. The platform includes input, output and processing modules. The neural network model is trained with input and output signals as training samples. Signals of the secondary path are generated by the addictive white noise. The neural network improves the accuracy of the secondary path identification model as shown in the training results, indicating that the nonlinear factors of secondary path can be described by the neural network. The coefficients computed offline are loaded into the DSP and taken as the filtering parameters of the input signals. Under 500 Hz and 500+800 Hz noise source, the noise control experiment of FIR secondary path model and traditional ANC algorithm is compared with a neural network model and the optimized ANC algorithm. The results show that the algorithm is effective with good performance under the low-frequency noise of single and two-mixed frequency.
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Key words:
- active noise control /
- nonlinear /
- secondary path /
- optimization /
- neural network
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图 3 附加白噪声法的次级通道辨识
Figure 3. Diagram of secondary path identification based on additive white noise
图 5 神经网络次级通道辨识结构图
Figure 5. Structure of secondary path identification based on neural network
图 18 (500+800) Hz噪声的 FxLMS控制实验功率谱密度图
Figure 18. PSD of FxLMS result with (500+800) Hz noise
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