改进深度神经网络的电力线脉冲噪声抑制与补偿算法

Improved deep neural network algorithm for power line pulse noise suppression and compensation

针对非线性抑制方法需要先验统计信息确定最佳门限且引起非线性失真的问题,提出一种改进深度神经网络(DNN)的电力线脉冲噪声抑制与补偿算法。首先通过DNN锁定脉冲噪声的位置,然后采用置零法清除该位置的数据,最后对处理后的信号进行非线性失真重构与补偿,并与其它算法进行了仿真对比实验。仿真结果表明:所提算法提升了DNN的识别率,降低了系统误码率,具有较好的鲁棒性。

To solve the problem that the nonlinear suppression method requires prior statistical information to determine the optimal threshold and causes nonlinear distortion,an improved deep neural network(DNN)algorithm for power line impulse noise suppression and compensation is proposed.Firstly,the position of pulse noise is locked by DNN,and then the data of the position is cleared by zerosetting method.Finally,the nonlinear distortion is reconstructed and compensated for the processed signal,and the simulation and comparison experiments are carried out with other algorithms.The simulation results show that the proposed method improves the recognition rate of the DNN,reduces the bit error rate of the system,and has good robustness.