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The filter-x least mean square (FxLMS) algorithm is widely used in active noise control (ANC) systems. However, because the algorithm is a feedback control algorithm based on the minimization of the error signal variance to update the filter coefficients, it has a certain delay, usually has a slow convergence speed, and the system response time is long and easily affected by the learning rate leading to the lack of system stability, which often fails to achieve the desired control effect in practice. In this paper, we propose an active control algorithm with nearest-neighbor trap structure and neural network feedback mechanism to reduce the coefficient update time of the FxLMS algorithm and use the neural network feedback mechanism to realize the parameter update, which is called NNR-BPFxLMS algorithm. In the paper, the schematic diagram of the feedback control is given, and the performance of the algorithm is analyzed. Under various noise conditions, it is shown by simulation and experiment that the NNR-BPFxLMS algorithm has the following three advantages: in terms of performance, it has higher noise reduction under the same number of sampling points, i.e., it has faster convergence speed, and by computer simulation and sound pipe experiment, for simple ideal line spectrum noise, compared with the convergence speed of NNR-BPFxLMS is improved by more than 95% compared with FxLMS algorithm, and the convergence speed of real noise is also improved by more than 70%. In terms of stability, NNR-BPFxLMS is insensitive to step size changes. In terms of tracking performance, its algorithm responds quickly to sudden changes in the noise spectrum and can cope with the complex control requirements of sudden changes in the noise spectrum.

Research on Narrowband Line Spectrum Noise Control Method Based on Nearest Neighbor Filter and BP Neural Network Feedback Mechanism